Remote Jobs Across Engineering, Product, and Data

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92 Jobs

69 Companies

AI Platform Engineer

DATAGROUP Stuttgart

Platform Engineer2 days ago

Full Time Remote Mid Level

Role Description - Du entwickelst und betreibst unsere souveräne LLM-Plattform inklusive OpenAI-kompatiblem Gateway, Modell-Serving und GPU-Infrastruktur. - Du implementierst kundenspezifische Wissensanbindungen (RAG) mit Vector-Datenbanken, Embeddings und Reranking. - Du stellst Kostenkontrolle, Mandantentrennung, Datenschutz und Guardrails sicher. - Du etablierst Monitoring und Observability für Performance, Qualität und Nutzung der KI-Dienste. - Du betreibst und entwickelst unsere containerbasierte Plattform auf Basis von Docker, Coolify und Kubernetes weiter. - Du automatisierst Deployments und verantwortest CI/CD-Prozesse für unsere Eigenentwicklungen. - Du betreust unsere Linux-, Cloud- und Datenbank-Infrastruktur und sorgst für Stabilität, Sicherheit und Verfügbarkeit. - Du baust Monitoring-, Logging- und Alerting-Lösungen auf und entwickelst diese weiter. - Du gestaltest die Architektur unserer Plattform- und KI-Services aktiv mit. - Du etablierst Infrastructure as Code und standardisierte Deployment-Prozesse. - Du sorgst für datenschutzkonforme und mandantensichere Betriebsmodelle. - Du dokumentierst Lösungen, teilst Wissen im Team und evaluierst neue Technologien im Bereich Platform Engineering und KI. Qualifications - Abgeschlossenes Studium der (Wirtschafts-) Informatik oder eine vergleichbare Qualifikation. - Mehrjährige Erfahrung im DevOps-, Plattform-Engineering- oder Cloud-/Infrastruktur-Umfeld. - Fundierte Praxiserfahrung im Betrieb containerbasierter Anwendungen und moderner Plattformen. - Erfahrung im Aufbau und produktiven Betrieb von LLM- und KI-Diensten, idealerweise in selbst gehosteten Umgebungen. - Sehr gute Kenntnisse in Kubernetes, Docker und Linux. - Erfahrung mit CI/CD-Pipelines, Cloud-Plattformen (insbesondere Azure) sowie PostgreSQL. - Praktische Erfahrung mit LLM-Gateways, RAG-Architekturen, Vector-Datenbanken und Modell-Serving-Lösungen. - Solide Python-Kenntnisse für Automatisierung und Integrationen. - Eigenständige und strukturierte Arbeitsweise mit hoher Eigenverantwortung sowie Sicherheits- und Datenschutzbewusstsein. - Ausgeprägtes Architektur- und Platformverständnis. Benefits - Offene und kooperative Unternehmenskultur sowie eine vertrauensvolle Arbeitsatmosphäre. - Umfassende Einarbeitung mit Unterstützung des gesamten AIS Teams. - Flache Hierarchien – kurze Entscheidungswege und hohe Gestaltungsfreiheit. - Strategische Gestaltungsrolle mit großem Einfluss auf die zukünftige Plattformlandschaft. - Mitarbeit an einem der spannendsten Technologiefelder der IT: souveränes LLM-Hosting und KI-Infrastrukturen. - Moderne Infrastruktur mit Kubernetes, Docker, CI/CD und Cloud-Technologien. - Möglichkeit, Standards, Prozesse und Architekturentscheidungen aktiv mitzugestalten. - Individuelles Weiterbildungsbudget für Konferenzen, Zertifizierungen und Fachtrainings. - Klare Karriere- und Entwicklungspfade. - Möglichkeit zur Spezialisierung als AI Platform Engineer bzw. LLMOps-Experte. - Kleines, fokussiertes und agiles Team mit kurzen Entscheidungswegen. - Offene Feedback-Kultur mit regelmäßigen 1:1-Gesprächen und Retrospektiven.

LLM OpenAI API Observability/Monitoring Docker Kubernetes CI/CD Linux Infrastructure as Code Azure PostgreSQL Python AI LLMOps

View details: AI Platform Engineer

Germany

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Director, Enterprise Architecture

HERE Technologies

The world's leading location platform company

Architect4 days ago

Other Remote LeadTeam 5,001-10,000H1B No Sponsor

Company Site LinkedIn

Role Description The Director, Enterprise Architecture plays a pivotal role within HERE’s Compute Foundation organization, leading the definition and evolution of enterprise-wide architectural strategy that underpins our AI ambitions and Enterprise business goals. This role oversees a small team of Enterprise Architects responsible for shaping coherent architectures across cloud platforms, developer enablement, corporate and employee applications as well as AI capabilities across these areas. The Director ensures that HERE’s technology landscape advances in a way that offloads complexity from engineering teams and employees, enabling them to move faster, while maintaining architectural consistency, reusability, and cost efficiency across horizontals. The EA team partners with Solution Architects, Software Architects, and engineering teams who design and implement project-specific solutions using these patterns, ensuring consistency and faster execution at scale. This role owns enterprise reference architectures, standards, and guardrails for AI capabilities across cloud platforms, developer enablement, corporate/employee applications, and shared services. The role defines and governs the end-to-end AI/GenAI lifecycle—including LLMOps/MLOps, evaluation, observability, and model/prompt/version controls—so AI systems are production-ready and measurable. A core focus of this role is enabling HERE’s AI journey—ensuring that AI, data platforms, and automation are architected, governed, and operationalized to deliver measurable improvements in productivity, reliability, and customer value for both HERE customers and internal teams. Qualifications - 15+ years of related experience with a Bachelor’s degree; or 12+ years and a Master’s degree; or a PhD with 8+ years experience; or equivalent experience. - Leading AI transformation and evolving Enterprise Architecture into a high impact function. - Defining and executing enterprise-scale architecture strategies. - Strong background in cloud platforms, distributed systems, DevOps, and platform engineering. - Demonstrated leadership in AI/ML and Generative AI architectures, including governance and responsible AI practices. - Experience working closely with product management, engineering, security, finance, and operations. - Ability to influence senior technical and business leaders through clear, pragmatic, and outcome-focused architecture. - Experience operating in large-scale, global, multi-product environments. Requirements - Lead, coach, and develop a high-performing Enterprise Architecture team aligned to HERE’s platform and product strategy. - Establish Enterprise Architecture as a strategic partner to Engineering, Product Management, and Platform Engineering leadership. - Contribute to the advancement of architectural and technical leadership practices across HERE R&D. - Define and maintain enterprise architecture vision, principles, standards, and target-state roadmaps. - Ensure architectural alignment across Compute Foundation services, platform components, and product-facing capabilities. - Drive architectural consistency across developer platforms, shared services, APIs, and internal tooling. - Support use of cloud-native, platform-oriented, and service-based architectures. - Own enterprise reference architectures, standards, and guardrails for platform and AI capabilities. - Lead architectural strategy for AI/ML and Generative AI across HERE. - Define reference architectures and reusable patterns for AI-enabled workflows, agentic systems, and automation. - Establish patterns for agent orchestration, tool access governance, human-in-the-loop controls, auditability, and security protections. - Define and govern the end-to-end AI/GenAI lifecycle. - Partner with FinOps to define cost attribution and budgets for AI workloads. - Define the EA engagement model, decision thresholds, and governance routes. - Chair or actively participate in architecture review boards and technical governance forums. - Define and track key metrics such as platform and service adoption, architecture health, AI utilization, and cost efficiency. Benefits - The expected base salary range for this position is $195,000 to $270,000 per year. - Actual compensation will be based on skills, experience, and other relevant factors. - This role is also eligible for a target annual performance bonus, based on company and individual performance. - Life at HERE includes a comprehensive benefits package designed to support your health and well-being, including: - Medical, dental, and vision insurance. - Retirement savings plans. - Paid time off and leave programs.

AI LLMOps Observability/Monitoring Distributed Systems AI/ML R

View details: Director, Enterprise Architecture

United States

$195K - $270K / year

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AI Solutions Engineer

Precisely US Jobs

Precisely is the leader in data integrity. We empower businesses to make more confident decisions based on trusted data through a unique combination of software, data enrichment products and strategic services. Focused on delivering outstanding innovation and support that helps customers increase revenue, lower costs and reduce risk Powers better decisions for more than 12,000 global organizations, including 95 of the Fortune 100 2500 employees unified by four core values: Openness, Determination, Individuality, and Collaboration Committed to career development for employees with opportunities for growth, learning, and building community "Work from anywhere" culture celebrating diversity in a distributed environment with a presence in 30 countries and 20 offices across 5 continents

Solutions Engineer6 days ago

Full Time Remote Mid LevelTeam 1,001-5,000

Role Description The Professional Services AI Solutions Engineer plays a critical role in accelerating the effective adoption of AI tools and capabilities across Precisely’s Professional Services team and is designed for bridging the gap between cutting-edge AI and billable service delivery. The role will collaborate with technical, business, and operational teams to identify AI use cases, support implementation, and measure impact for Professional Services, ensuring AI solutions are practical, scalable, and aligned with business objectives. The Analyst is comfortable translating complex AI concepts into actionable insights and supporting stakeholders as AI becomes part of everyday workflows. What you will do: - Building AI Deployment Accelerators: - Independently build AI agents to automate complex data mapping, schema conversion, and code migration tasks. - Design "Configuration Agents" that auto-generate documentation and environment setups for product deployments. - Use Case Identification & ROI Strategy: - Audit Professional Services workflows to identify high-impact AI opportunities. - Perform cost-benefit analyses, translating technical metrics into "hours saved" and "margin improvement" for leadership. - Hands-on Agentic Engineering: - Design and code autonomous AI Agents using frameworks like Lang Graph, Crew AI, or Semantic Kernel. - Build systems capable of multi-step reasoning, self-correction, and tool-calling. - Team Enablement & Training: - Collaborate with Central AI Team's AI Enablement Lead on PS-specific training content. - Self-Driven Governance & Scaling: - Implement LLM Ops for PS-specific agentic solutions, operating within Precisely's AI Architecture Standards and in coordination with the Central AI Team's AI Platform Engineer for tooling, evaluation standards, and model governance. - Agentic Solutions: - Lead the strategy, design, and delivery of agentic service offerings in Professional Services by leveraging MCP servers to connect AI agents with enterprise data and software solutions. - Own customer adoption of MCP based solutions, driving scalable, governed, and LLM agnostic agent implementations that maximize existing automation investments. Qualifications - Bachelor’s degree in computer science, Information Systems, Data Analytics, Business, or a related field. - 3–5 years of experience supporting Professional Services delivery, consulting initiatives, or technology-enabled programs in a software or SaaS environment. - Experience supporting technology adoption, change management, or enablement across cross-functional teams. - Strong analytical and problem-solving skills with the ability to interpret data and derive actionable insights. - Excellent written and verbal communication skills, with the ability to translate complex concepts for diverse audiences and non-technical stakeholders. - Ability to simplify complex LLM concepts into actionable training for consultants, analysts, and partners. - Experience working with stakeholders at multiple levels, including technical and non-technical partners. - AI Enablement & Training background and excellent presentation skills a must. Requirements - Strong proficiency in Python, with hands-on experience building or enhancing AI-enabled workflows or internal accelerators. - Practical experience designing and implementing generative AI solutions, including Retrieval-Augmented Generation (RAG), vector databases, and API-based integrations. - Experience working with AI agent frameworks and orchestration tools such as LangGraph, CrewAI, Semantic Kernel, or LlamaIndex. - Experience using enterprise LLM platforms (e.g., Anthropic Claude, OpenAI models), with sound judgment in evaluating outputs, limitations, and risk. - Solid working knowledge of AI/ML concepts and automation platforms sufficient to support enablement, training, and real-world solution delivery. AI Skills/Knowledge - Ability to identify and improve manual or repetitive Professional Services processes using AI or automation. - Clear analytical and communication skills, with the ability to explain AI-driven outcomes to consultants and delivery stakeholders. - Comfort using generative AI tools (e.g., Microsoft Copilot, ChatGPT, Claude) and a growth mindset aligned to an AI-first organization.

AI AI Agents LLMOps Data Analytics Python LlamaIndex Anthropic Claude OpenAI API AI/ML

View details: AI Solutions Engineer

United States

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Senior Director, Enterprise Architecture

Foot Locker

Architect7 days ago

Full Time Remote Lead

Role Description The Senior Director, Enterprise Architecture (EA) leads a modern, AI-driven and execution-oriented enterprise architecture capability. This role focuses on translating business and technology strategy into working guardrails, reusable platform patterns, and measurable outcomes. The emphasis is on hands-on, practical architecture that accelerates delivery, reduces risk and complexity, and embeds architectural thinking directly into product and engineering workflows. What Success Looks Like (Outcomes) - Enterprise Architecture is embedded in delivery, not operating as an ivory-tower or documentation-only function. - AI and platform decisions are standardized through enterprise reference architectures and paved roads. - Architecture standards, guardrails, and decisions are enforced through automation and AI-enabled governance. - Measurable improvements are realized in delivery rework reduction, modernization velocity, solution quality, cost optimization, and risk reduction. Responsibilities - AI-Driven Enterprise Architecture - Define and evolve the enterprise AI architecture across user experience, governance, agent orchestration, verification and safety, models and LLMOps, data and knowledge, and runtime layers. - Establish responsible AI, security, privacy, observability, and cost controls as architectural defaults across platforms. - Partner with engineering and product leadership to ensure AI solutions are scalable, reusable, governed, and enterprise-safe. - Executable Enterprise Architecture - Drive adoption of executable EA practices where architecture is machine-readable, automatable, and enforceable. - Establish architecture-as-code and policy-as-code patterns using version-controlled standards, ADRs, and automated CI/CD checks. - Shift governance left into delivery pipelines, reducing reliance on manual review boards. - Applied Architecture & Delivery Enablement - Work hands-on with product and engineering teams on priority initiatives. - Participate directly in roadmap planning, backlog refinement, solution design, and architectural tradeoff discussions. - Ensure modernization efforts leverage standard cloud-native, API-first, and event-driven patterns. - Standards, Guardrails & Architecture Community - Own enterprise architecture standards delivered as practical assets: templates, reference implementations, and guardrails. - Operate architecture through an inner-source model that encourages contribution from architects and engineers. - Lead and mature the architecture community of practice to drive consistency, reuse, and continuous learning. - Establish architecture governance frameworks and ensure adherence to security, privacy, and regulatory standards such as SOC 2, GDPR, and HIPAA. - Lead architecture governance forums and define principles, standards, and best practices to ensure consistency and compliance. - Landscape Optimization & Value Realization - Use architectural insight and AI-driven analysis to identify cost, risk, and simplification opportunities. - Establish transparency into application lifecycle, dependencies, and modernization readiness. - Translate architecture insights into tangible financial and operational outcomes. Qualifications - Minimum Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience. Master’s degree preferred. - 10+ years of experience in enterprise, platform, solution, or domain architecture roles within large-scale, complex organizations. - Demonstrated leadership in AI-driven architecture, including enterprise AI platforms, generative AI, LLM-based systems, RAG, embeddings, prompt orchestration, or agent-based solutions. - Strong understanding of modern AI system architecture, including model lifecycle management, LLMOps/MLOps, knowledge architectures, and human-in-the-loop decision patterns. - Proven experience defining and operationalizing enterprise guardrails such as architecture standards, reference architectures, and automated governance. - Hands-on experience with cloud platforms, data ecosystems, and modern integration patterns (API-first, event-driven, domain-oriented). - Ability to translate AI and architecture tradeoffs into business outcomes including cost optimization, risk reduction, and delivery acceleration. Leadership & Interpersonal Skills - Strong executive presence with the ability to influence senior stakeholders without formal authority. - Build and mentor a high-performing architecture team, fostering innovation, collaboration, and alignment across the enterprise. - Ability to translate complex technical, architectural decisions and trade-offs effectively to technical and non-technical stakeholders, including executive leadership. - Collaborative, pragmatic, and delivery-oriented leadership style. - Strong mentoring, coaching, and architecture community-building mindset. Benefits - The annual base salary range is $200,000 - $265,000. - This role is also eligible to receive short and long term incentives that align with individual and company performance. - Salary will be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, and alignment with market data. - Foot Locker Benefits: - Employee Discount - Paid Time Off - Medical | Dental | Vision Coverage - 401(k) | Roth 401(k) - Stock Purchase Plan - Life Insurance - Flexible Spending Account - Opportunities for Advancement - Tuition Reimbursement for Qualified Courses - Strong Company Culture - Employee Resource Groups

AI LLMOps Observability/Monitoring CI/CD LLM

View details: Senior Director, Enterprise Architecture

United States

$200K - $265K / year

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Data Scientist PL/SR

DOT Group

Data Scientist9 days ago

Full Time Remote Senior

Role Description Como Cientista de Dados, você será fundamental para transformar dados em inteligência ativa. Seu foco será o desenvolvimento e a implementação de modelos de classificação, previsão e soluções baseadas em IA Generativa, integrando-as aos nossos produtos, processos internos e iniciativas institucionais. Aqui, você atuará focado no core de Data Science: - Modelagem e Análise: desenvolver, validar e apoiar a implantação de modelos de classificação e previsão (Machine Learning) para otimizar processos e alavancar produtos de dados. - IA Generativa na Prática: desenvolver e implementar aplicações baseadas em Grandes Modelos de Linguagem (LLMs) para automação e novas funcionalidades de produto. - Visão de Produto: apoiar a tradução de dores de negócio em problemas de Data Science, ajudando a definir hipóteses, métricas de sucesso e critérios de avaliação alinhados aos objetivos da instituição. - Ciclo de Vida dos Modelos: acompanhar a saúde dos modelos em produção, monitorando métricas de desempenho, qualidade e possíveis desvios. - Colaboração Multidisciplinar: atuar em parceria com os times de Produto, Engenharia de Dados e BI para integrar soluções analíticas ao ecossistema da empresa. Qualifications - Prática em Machine Learning: experiência no ciclo de modelos de classificação e regressão/previsão (da exploração dos dados e validação até o acompanhamento do modelo). - Ecossistema Python: conhecimento sólido na linguagem e em suas principais bibliotecas de manipulação e modelagem. - Desenvolvimento com LLMs: experiência prática na construção de aplicações utilizando LLMs e frameworks de suporte (como LangChain ou LlamaIndex), compreendendo conceitos de arquiteturas como RAG. - Manipulação de Dados: boa autonomia em SQL para extração, transformação e análise exploratória de dados. - Noções de MLOps e LLMOps: conhecimento sobre boas práticas de versionamento de código/modelos e ferramentas de monitoramento de experimentos. - Noções de Produtos de Dados: compreensão de como um modelo analítico se integra e gera valor dentro de um produto ou processo de negócio. - Cloud Computing: vivência ou familiaridade com serviços gerenciados de IA/ML em ambiente de nuvem, preferencialmente AWS. Requirements - Conhecimento em técnicas de Engenharia de Prompts e testes com múltiplos modelos de linguagem. - Noções de segurança, governança e uso ético de Inteligência Artificial. - Experiência prévia atuando em times multidisciplinares de produtos digitais. Benefits - Vale alimentação e/ou refeição (R$837) - Inclusive nas férias - Vale transporte - Seguro de vida - Auxílio home office (R$100) - Auxílio creche para crianças de até 6 anos - Headhunter (valor por indicação de colaborador) - Indicação de clientes (valor por indicação de clientes) - Plano de saúde integral, sem coparticipação (com terapia online gratuita) - Convênio odontológico - Caju+ (assistência psicológica, assistência jurídica, consultoria financeira) - Wellhub (plataforma de saúde e bem estar) - DOT Break (Um dia de folga extra no ano, para usar como quiser) - Convênio Farmácia - Licença maternidade estendida de 6 meses - Licença paternidade estendida de 20 dias - Onhappy - Ofertas exclusivas de passagens e hospedagens - FeelingDOT sobre temas diversos (workshops sobre autoconhecimento, diversidade, inclusão e equilíbrio) - Mimos e gifts em datas comemorativas - Verba de Sophia de R$ 2.000,00 (auxílio educação) - Horas de Sophia (40 horas/ano em capacitação da sua escolha) - Verba Survival (auxílio educação no negócio) - DOTAcademy com cursos gratuitos - Mobilidade interna - DOTHUB (desenvolvimento de lideranças) - Cultura de feedback - DOTKnows (peer learning) - Cousera - Ilimitado - Home office: trabalhe de qualquer lugar do Brasil - Horário flexível - Nosso regime é de 40h semanais - Aprendizado contínuo - Pessoas que é bom de estar perto - Autonomia com responsabilidade - Local para refeições (presencial) - Lanches, snacks, frutas, chás e café passado (presencial) - Área de convivência (presencial) - Momentos de integração e festas

AI/ML Python LangChain LlamaIndex SQL LLMOps AWS R

View details: Data Scientist PL/SR

Brazil

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Head of Data & AI

JustMarkets

Data Scientist10 days ago

Full Time Remote LeadTeam 201-500H1B No Sponsor

Company Site LinkedIn

Role Description We are looking for a highly motivated and results-driven Head of Data & AI to join our team full-time. In this strategic role, you will shape the vision, architecture, and delivery frameworks for Data Engineering, Data Science, Quant and Data Analytics, unifying these teams into a single, high-impact function. This is a key leadership position, guiding both technical and managerial directions for our data organization. We drive fintech innovation through deep analytical expertise and a data-first, engineering-driven approach. Key Responsibilities - Data Engineering - Design and evolve a scalable, reliable, and maintainable data platform architecture - Oversee development of robust ETL/ELT pipelines and real-time data streaming systems - Establish engineering best practices, including code reviews, CI/CD, data contracts, and observability - Lead technology selection and resource planning across ClickHouse, Spark, and supporting infrastructure - Ensure data quality through monitoring, alerting, SLA ownership, and remediation processes - Manage infrastructure costs and drive optimization across storage, compute, and cloud resources - Artificial Intelligence & Large Language Models - Define and develop the company's overall AI direction and roadmap, aligning initiatives with long-term business strategy - Ensure operational stability and observability of ML services - Define and drive LLM strategy, including identifying high-value use cases, evaluating model providers (OpenAI, Anthropic, open-source), and leading end-to-end implementation - Architect and oversee LLM-powered products, including RAG pipelines, AI agents, and intelligent automation workflows integrated into core business processes - Establish MLOps/LLMOps best practices, including model versioning, evaluation frameworks, prompt management, and drift/hallucination monitoring - Drive responsible AI governance, including bias detection, explainability (SHAP, LIME), fairness auditing, and compliance with emerging AI regulations - Evaluate and integrate vector databases (Pinecone, Weaviate, pgvector) and embedding strategies to power semantic search and knowledge retrieval - Champion AI-assisted development practices (e.g., GitHub Copilot, Cursor) and foster an AI-augmented engineering culture across data teams - Data Analytics & Quant - Drive advanced analytics, strategy, and Quant development - Partner with stakeholders to translate complex business challenges into data-driven solutions - Define and own key metrics, dashboards, and reporting frameworks to support executive and board-level decision-making - Lead experimentation practices to validate business impact of models and initiatives - Mentor teams, setting technical standards and career development paths Qualifications - 7+ years of hands-on experience across Data Engineering, Data Science, Quant, Data Analytics, delivering end-to-end solutions - 3+ years of managerial experience, leading data teams - Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, Engineering, or a related field - Strong programming skills in Python, with experience writing clean, production-grade code - Solid understanding of software engineering best practices (CI/CD, testing, code reviews, clean architecture) - Deep understanding of core ML algorithms: regression, gradient boosting, time series, etc. - Practical experience with ML libraries and platforms (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch) - Strong foundation in mathematical statistics, probability theory, and quantitative modeling - Proficient in SQL and experience with analytical and OLAP databases - English Upper-Intermediate Nice to Have - Background in trading or fintech - Experience analyzing and modeling time series or high-frequency data - Familiarity with anti-fraud systems, risk modeling, or portfolio analytics - Practical experience with integration of LLM with corporate systems for internal users Benefits - 20 paid vacation days per year - 10 paid sick leave days per year - Public holidays according to current legislation - Medical insurance - Opportunity to work remotely - Professional education budget - Language learning budget - Wellness budget (gym membership, sports gear and related expenses)

AI Data Engineering Data Analytics ETL CI/CD Observability/Monitoring Apache Spark AI/ML LLM OpenAI API AI Agents LLMOps Pinecone Weaviate GitHub Python scikit-learn TensorFlow PyTorch SQL

View details: Head of Data & AI

Worldwide

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Senior Data Engineer, AI

EPAM

Data Engineer11 days ago

Full Time Remote Senior

Role Description We are seeking a Senior Data Engineer to architect, develop, and operationalize enterprise-grade AI agents, multi-agent workflows, and AI/ML solutions leveraging Python, Azure AI Foundry, Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, and Strands SDK. In this role, you will partner with architects and data teams to advance solutions from prototype to scalable, dependable production. - Architect, develop, and maintain enterprise-grade AI agents and multi-agent workflows for business use cases - Incorporate and operationalize AI/ML models and agent workflows within scalable production environments - Build orchestration with Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, and/or Strands SDK - Apply RAG, grounding, and prompt optimization to enhance response quality and minimize hallucinations - Connect agents with internal and external systems through APIs and connectors - Create and deploy error handling, fallback flows, and escalation paths - Conduct testing and assessment of agents and automation workflows - Work with architects and data teams to convert requirements into deployable AI solutions - Assist with deployment, CI/CD, and release management using Azure DevOps - Track and steadily enhance agent performance, reliability, and cost efficiency Qualifications - Skilled in Python development for AI/agent solutions, paired with strong general scripting ability - Practical experience creating agents and multi-agent workflows using one or more orchestration frameworks: Semantic Kernel / Microsoft Agent Framework, LangGraph, LangChain, AutoGen, Strands SDK - Deep knowledge of Azure AI Foundry for developing, deploying, and running agents at scale (hosted agents, agent service, memory, evaluation) - Strong grasp of LLM concepts: prompt engineering, RAG, grounding, tool/function calling, hallucination mitigation, and evaluation - Proficiency in API integration (REST APIs, JSON, authentication through OAuth2 / API keys) - Ability to design agent behavior: state and memory management, tool use, error handling, fallback, and escalation paths - Capacity to work with architects and data engineering teams to deliver scalable, production-grade solutions - English proficiency at B2 level or above Requirements - Experience with Azure AI Services and the wider Azure stack (Blob Storage, Container Services) - Familiarity with multi-cloud agent deployment (e.g., AWS Bedrock via Strands SDK) - Knowledge of Model Context Protocol (MCP) for tool/connector integration - Understanding of LLMOps / MLOps: monitoring, evaluations, and cost/latency optimization for AI Agents - Background with vector databases, retrieval infrastructure for RAG, and Docker - Proficiency in Microsoft Graph API integration and delivering within enterprise or regulated/validated environments (e.g., GxP), including governance and observability tooling

AI Agents AI/ML Python Azure LangChain SDK CI/CD Azure DevOps LLM REST API JSON OAuth 2.0 Data Engineering Amazon Bedrock LLMOps Observability/Monitoring Docker

View details: Senior Data Engineer, AI

Turkey

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Data & AI Solutions Architect

Sphere Partners

Solutions Engineer11 days ago

Full Time Remote Mid Level

Role Description We are looking for an AI Solutions Architect to join our team and help our customers improve their service. - Develop Generative AI solutions utilizing GCP services, across various client teams; - Contribute to the development and application of Large Language Models (LLMs) and Generative AI; - Explore new areas within LLMs and Generative AI, translating research into practical applications; - Communicate complex technical concepts and findings to non-technical stakeholders; - Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems; - Stay updated on the latest advancements in generative AI technologies and methodologies; - Optimize and fine-tune generative models for performance and efficiency; - Troubleshoot and resolve issues related to generative AI models and implementations; - Create and maintain documentation for generative AI models and their applications; - Deploy and monitor Machine Learning models, focusing on generative AI and LLM-based agents; - Design and develop prototypes in collaboration with IT practitioners and client stakeholders; - Evaluate new tools and technologies, selecting appropriate ones for efficient and quality deliverables; - Demonstrate confidence and ability to work in ambiguous situations; - Influence business decisions by providing recommendations and trade-offs; - Engage in continuous research and learning to attain in-depth knowledge of capabilities, limitations, internal architecture, and applications of a broad range of trending tech stacks, presenting essential comparisons, pros/cons, and target use cases; - Assist leadership in building or upgrading reference architectures, developing new service lines or enhancing existing ones, and designing and implementing programs to align delivery teams with new or enhanced service offerings; - Lead training programs to help the next generation gain technical expertise. Qualifications - At least 3 years of experience in creating AI solutions using Databricks (certification is a plus); - Experience with generative AI frameworks, such as LlamaIndex and Langchain; - Familiarity with generative AI models from OpenAI, Google, or open-source alternatives (e.g., Llama, Claude, Hugging Face); - Minimum of 5 years of programming experience in Python and SQL; - Proficiency in diverse data storage systems, including RDMS and NoSQL; - Expertise in utilizing major cloud platforms, with a strong emphasis on GCP (minimum 2-3 years of experience) and knowledge of AWS; - Understanding of neural network architectures, such as Generative Adversarial Networks (GANs) and Transformers; - Experience using TensorFlow, PyTorch, and Keras for designing, training, and validating AI models; - Familiarity with CI/CD solutions in the context of MLOps and LLMOps, including automation with Infrastructure as Code (IaC) tools like Biceps or Terraform; - Skills in data preprocessing and feature engineering for AI model training; - Knowledge of designing and explaining data pipelines, ML pipelines, and ML training and serving methodologies; - Experience in deploying ML models using native infrastructure (e.g., Databricks) or third-party MLOps platforms; - Understanding of specific domains or industries and the application of NLP/LLM solutions; - Demonstrated innovative mindset and problem-solving abilities; - Awareness of AI standards and the ability to assess and mitigate technology risks; - Excellent communication and presentation skills. Benefits - Competitive and rewarding salary; - Benefits package; - Intellectually and creatively stimulating work environment; - Flexibility; - Unique international travel opportunities.

AI GCP AI/ML LLM Databricks LlamaIndex LangChain OpenAI API Hugging Face Python SQL NoSQL AWS TensorFlow PyTorch Keras CI/CD LLMOps Infrastructure as Code Terraform

View details: Data & AI Solutions Architect

Worldwide

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Job Closed

Lead Solutions Architect

Humana

Architect11 days ago

Full Time Remote LeadTeam 10,001+Since 1961H1B Sponsor

Company Site LinkedIn

Role Description The Lead Solutions Architect works with user groups to solve business problems with available technology including hardware, software, databases, and peripherals. The Lead Solutions Architect works on problems of diverse scope and complexity ranging from moderate to substantial. The Lead Solutions Architect provides architecture leadership for CenterWell Home Health programs and platforms, shaping conceptual and reference architectures, governing solution designs, and aligning delivery teams to cloud and data strategies. The scope includes high-priority initiatives as well as interoperability and provider-data integrations that span CenterWell and Humana Insurance. You will operate within CenterWell IT – Cross-CenterWell Architecture. You will collaborate with product, engineering, EA Activation, security, data, and operations. You will engage governance forums to enable Integrated Health across CenterWell and Humana Insurance. Key Activities - Quickly conduct structured knowledge transfer with existing architects and relevant stakeholders to capture critical in-flight designs and decisions. - Review current initiatives and establish an architectural roadmap aligned with organizational priorities. - Develop or refine reference architectures and design patterns for core platforms and solutions. - Collaborate with governance and compliance teams to validate designs against enterprise standards and regulatory requirements. - Define integration strategies and solution blueprints for key systems and data flows. - Establish architecture review processes and decision forums to support delivery readiness and quality assurance. Demonstrated Depth In - Agent orchestration (tools/function calling, planning, memory, multi-agent collaboration) - Enterprise guardrails (security, compliance, responsible AI) - Robust MLOps/LLMOps practices for observability, evaluation, and cost/performance optimization - Proven ability to integrate AI systems with event-driven microservices, vector databases/feature stores, and CI/CD pipelines, while leading reference architectures and cross-cloud patterns that scale reliably. - Executive-level communication and stakeholder management across product, engineering, and enterprise functions. Core Responsibilities - Architectural Leadership: Own end-to-end solution architecture for Home Health initiatives, translating business outcomes into composable designs across systems of experience, operations, and insight. - Reference Architectures & Patterns: Author and maintain reference architectures for data platform, clinical documents NLP, RPA/automation, and interoperability patterns; steward pattern reuse across CenterWell when applicable. - Governance & Compliance: Lead architecture checkpoints and risk reviews; ensure designs conform to enterprise guardrails and privacy/security requirements. Participate in governance forums (e.g., ARB, TRB, MOR). - Delivery Enablement: Partner with product and engineering to convert conceptual designs into executable architectural runways; de-risk delivery with measurable non-functional requirements (NFRs). - Data & Integration Strategy: Define integration contracts and domain boundaries for provider data, EMR, and Snowflake/Databricks/Azure data platforms; ensure observability, lineage, and data protection. - Stakeholder Engagement: Collaborate with CenterWell leaders, EA Activation, security, and segment CIO teams; communicate decisions clearly to executives and delivery teams. Qualifications - Bachelor's Degree in Computer Science or a related field. - 8 years of progressive information technology experience directly related to architecture/engineering, information security, or other specialized technology field. - 5+ years of solution architecture experience with healthcare delivery or payer/provider ecosystems; proven delivery within regulated environments (HIPAA/PHI). - Hands-on architecture across Azure services and Snowflake/Databricks (data lake/data platform patterns, data sharing, identity & access, vault/secrets management). - Demonstrated leadership producing reference architectures and governing solution designs across multiple teams. - Strong competency in APIs/event-driven integration, security models, and NFRs (availability, performance, resilience). - Generative & Agentic AI, Multi-Cloud is a specialty of a seasoned Solutions Architect with hands-on experience delivering generative and agentic AI on Google Cloud, Azure, and AWS. Preferred Qualifications — Core Home Health - Experience with clinical document NLP pipelines and automation/RPA in Home Health workflows (eligibility, authorization). - Familiarity with Home Healthcare solutions conceptual architecture and operating models and artifacts; ability to evolve them with measurable outcomes. - Knowledge of enterprise identity/access management flows and Azure group governance within tenants. - Experience participating in portfolio governance and activation forums (e.g., ARB, TRB, MOR) and translating those decisions into architectural runways. Preferred Qualifications — Agentic AI Skills - Agent orchestration: planning, tool/function calling - Long/short-term memory and context management - Multi-agent collaboration and role assignment - Human-in-the-loop review and overrides workflow - Safety guardrails, policy enforcement, governance - Telemetry: tracing, evaluation, A/B testing - Reliability: retries, fallbacks, deterministic handoffs - Tool ecosystem integration (APIs, MCPs) Work-At-Home Requirements - Must have the ability to provide a high-speed DSL or cable modem for a home office. - A minimum standard speed for optimal performance of 25×10 (25 Mbps download × 10 Mbps upload) is required. - Satellite and Wireless Internet service is NOT allowed for this role. - A dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information. Travel While this is a remote position, occasional travel to Humana's offices for training or meetings may be required. Scheduled Weekly Hours 40 Pay Range The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc. $142,300 - $195,700 per year This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance. Benefits - Medical, dental and vision benefits - 401(k) retirement savings plan - Time off (including paid time off, company and personal holidays, paid parental and caregiver leave) - Short-term and long-term disability - Life insurance - Many other opportunities

AI LLMOps Observability/Monitoring Performance Optimization Microservices CI/CD RPA Snowflake Databricks Azure Segment HashiCorp Vault GCP AWS A/B Testing

View details: Lead Solutions Architect

United States

$142.3K - $195.7K / year

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Job Closed

Data Engineer

Livefront

We help companies grow by creating digital products people love.

Data Engineer12 days ago

Full Time Remote Mid LevelTeam 11-50H1B Sponsor

Company Site LinkedIn

Role Description We're looking for an outstanding Databricks Data Engineer to join our growing data practice — someone who builds the data and AI foundations that digital products and intelligent experiences run on. Who you are: - You are a Databricks-focused Data Engineer who understands that great data platforms are only as valuable as the products, AI workflows, and experiences they enable. - You bring deep, production-grade expertise across the Databricks platform and know how to connect platform capabilities to real business outcomes. - You thrive in ambiguity and can quickly assess a client's data landscape to recommend and implement the right solutions. - You understand that in consulting, your Databricks depth is most valuable when it connects platform capabilities to the products and experiences clients actually use. - You're as comfortable in a product design conversation as you are building a DLT pipeline. - You excel at translating complex data challenges into clear technical requirements and can confidently navigate conversations with everyone from data scientists to executives. - Your engineering principles are mature and grounded in real-world experience across various industries and scales. - You have an interest in and a curiosity about data platforms and the latest advances in data technology. What you will be doing: - Design and build production data pipelines using Lakeflow Declarative Pipelines, Autoloader, and Structured Streaming, with end-to-end ownership of ingestion, transformation, data quality expectations, and CI/CD deployment via Declarative Automation Bundles. - Architect and implement Lakehouse solutions on Databricks — medallion architecture, Delta Lake, Unity Catalog — tailored to the client's analytics, AI, and application needs. - Build and maintain Databricks transformation layers — DLT pipelines, PySpark notebooks, and dbt — with data quality constraints and SLAs baked in. - Design and maintain the data and AI foundations — Unity Catalog, Feature Store, MLflow, and Model Serving — that power production ML, agent workflows, and AI-enabled digital products. - Collaborate with product and backend engineers to design data models, APIs, and application data contracts — ensuring the platform serves the product, not just the warehouse. - Consult with clients to understand their data challenges, develop data strategies, and implement sustainable solutions. - Adapt your approach based on project needs — sometimes leading data architecture discussions with clients, other times supporting internal teams with specialized data expertise. - Work within multi-cloud environments — primarily AWS and Azure — anchoring data platform recommendations around Databricks where it fits the client's architecture and goals. - Champion data governance through Unity Catalog — access control, lineage, data quality policies, and compliance — as a first-class part of every engagement, not an afterthought. - Design data-to-application architectures — including Lakebase-backed services and Databricks Apps — that connect governed data to AI workflows, digital products, and user-facing experiences. - Help build Livefront's Databricks practice — contributing to accelerators, internal enablement, certification goals, and Databricks partner go-to-market materials alongside delivery work. Qualifications - 3-5 years of data engineering experience with at least 2 years in production Databricks environments, preferably in a consulting or client delivery context. - Solid working knowledge of AWS and Azure cloud services relevant to Databricks deployments — storage, networking, IAM, and compute — with GCP familiarity a plus. - Deep, production-grade Databricks expertise: Lakeflow Declarative Pipelines, Autoloader, Structured Streaming, Lakeflow Jobs, Unity Catalog (including fine-grained access control and lineage) — demonstrated through shipped production workloads, not prototypes. - Proven experience designing Lakehouse architectures — medallion patterns, Delta Lake table design, partitioning, Z-ordering, and query optimization — at production scale. - Hands-on experience with data pipeline testing, observability, and CI/CD for data — including unit testing, data quality frameworks, and version-controlled deployments via Git and Declarative Automation Bundles. - Strong proficiency in SQL and Python, with the ability to write clean, performant, and maintainable code. - Understanding of data modeling, schema design, and query optimization. - Excellent communication skills with the ability to explain complex data concepts to both technical and non-technical stakeholders. - Strong problem-solving skills with the ability to navigate ambiguous requirements and deliver pragmatic solutions. - Above-average discipline and personal organization skills. - Obvious comfort with critique and peer review in the context of an iterative development process. - A demonstrated hunger for personal and professional growth. - A self-evident love and care for the craft of data engineering. Requirements - Bonus points if you have worked with real-time streaming technologies (Kafka, Kinesis, etc.). - Have hands-on experience with alternative cloud data platforms — useful context for migrations and competitive assessments, though Databricks is our primary platform focus. - Have experience in healthcare or fintech domains. - Have hands-on experience with MLOps or LLMOps on Databricks — MLflow experiment tracking, model registry, Model Serving endpoints, or Vector Search for RAG pipelines. - Have experience with Java, Go, or Scala. - Have strong illustration skills for technical diagramming and data architecture documentation. - Speak, write, and/or educate publicly about data engineering topics. - Have contributed to open-source data projects. - Hold or are actively pursuing a Databricks certification (Data Engineer Associate or Professional, or Apache Spark Developer) — we treat these as meaningful signals of platform depth, and they directly support our Databricks partner growth goals. - Have experience with Databricks Apps, or Lakebase — early familiarity with where the Databricks platform is heading is a strong differentiator. Benefits - You want to work with passionate and talented people who are always looking for ways to make things better. - You desire a work environment where respect, mutual trust, and egoless collaboration are paramount. - You want colleagues who take their work seriously but not themselves, and who know how to let loose and have a good time. - You like being part of a team with a reputation for excellence that gives back to the community by educating, mentoring, and sponsoring. - You want to work on products and accounts that have outsized impact and reach. - You believe in sweating the details, giving a damn about quality, and taking pride in going the extra mile. - You want to help build a data practice specialization from the ground up — shaping how we go to market with Databricks, what we build as accelerators, and what it means to do this kind of work at a digital product company. What to expect - When applying, please include a short note about yourself, a summary of your work experience, and a link to any public profiles you actively maintain (e.g., GitHub, LinkedIn, etc). - Our hiring process moves quickly and consists of several stages for candidates who capture our attention with their initial submission, sometimes including but not limited to a short preliminary phone interview, a series of video interviews, and a short take-home exercise, which you'll have up to a week to complete. - Compensation is $120,000 - $145,000. Additional information We go out of our way to evaluate all employees and job applicants equally based on merit, competence, and qualifications. We encourage candidates from all backgrounds to apply and consider all qualified applicants. Don't worry, every application will be reviewed by a human.

Databricks AI Excel CI/CD Unity PySpark dbt MLflow AI/ML AWS Azure Data Engineering Amazon IAM GCP Observability/Monitoring Git SQL Python Apache Kafka Amazon Kinesis LLMOps Java Scala Apache Spark GitHub

View details: Data Engineer

United States

$120K - $145K / year

Apply

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