Machine Learning Engineer Remote Jobs in Ohio (US)
This page tracks remote machine learning engineer openings that are location-eligible for Ohio.
This page tracks remote machine learning engineer openings that are location-eligible for Ohio.
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• Define and own Guidewire's enterprise AI architecture vision • Architect and lead delivery of production AI systems • Own the full-stack AI architecture • Help define enterprise AI security and governance standards • Lead complex, multi-team AI programs end to end • Help drive technology evaluation and build-vs-buy decisions • Partner with Data Governance, Security, Legal, and Compliance teams • Mentor senior engineers and foster a culture of technical excellence
• Design, build, and operate enterprise AI systems across our client portfolio. • Work end-to-end across the AI stack — from inference engines and platform infrastructure up through application-level engineering. • Lead end-to-end design, build, and operation of AI systems on AI Factory platforms across multiple client engagements. • Engineer and tune LLM inference serving stacks — primary depth in vLLM with breadth across the inference ecosystem — for client latency, throughput, and cost targets. • Tune inference performance through KV cache management, paged attention, batching strategies, and Dynamo-based disaggregated serving. • Architect and operate MLOps pipelines covering model lifecycle, registries, deployment, rollback, and observability. • Design and engineer RAG applications on top of vector databases. • Build and tune prompt-engineering patterns at production scale. • Engineer high-performance storage and networking for AI workloads. • Operate Kubernetes clusters underpinning AI workloads. • Build and maintain container images, registries, and CI/CD pipelines for AI/ML services. • Implement monitoring, alerting, logging, and capacity planning across the AI stack. • Harden environments to meet client security and compliance requirements. • Lead troubleshooting across various environments and technologies. • Engage directly with client stakeholders — technical and executive — to communicate status, root cause, options, and recommendations. • Mentor and code-review work from less senior engineers; raise the technical bar of every engagement you join. • Author runbooks, reference architectures, and knowledge base content; lead client knowledge transfer and enablement sessions. • Participate in on-call rotation and incident response for production AI workloads. • Contribute reusable patterns, tooling, and reference designs back to the practice.
This is the ideal home for a builder who loves shipping real software fast, thrives in autonomy, and wants to work on massive enterprise modernization without the burden of maintaining archaic legacy code. You will have a direct hand in shaping how AI-driven engineering is executed at scale.
Role Description The landscape of software development is undergoing a fundamental shift, and so is the way we build technology. We are pioneering the "Builder" role—a hybrid evolution designed for elite engineers who recognize that AI-first workflows can radically compress traditional development timelines. We are not looking for engineers who merely build AI solutions (like standard RAG apps or basic agent fine-tuning). We are seeking seasoned, masterful software engineers who were highly successful before the introduction of Large Language Models (LLMs), and who have now actively evolved their personal development workflows using AI to massively accelerate delivery. You will join a high-impact engineering delivery team initially aligned to a major digital transformation for a massive enterprise client, or leading a greenfield, AI-first operations platform build designed to completely replace a legacy system. What You'll Do - Accelerate the Lifecycle: Take complex business requirements and utilize advanced AI-assisted development tools (such as Claude, GitHub Copilot, etc.) to rapidly spin up MVPs and functional prototypes in hours rather than weeks. - Full-Lifecycle Engineering: Own the architectural planning, documentation, implementation planning, code generation, and product requirement/user story refinement process. - Client-Facing Impact: This role requires high emotional intelligence and exceptional communication. You will regularly interact with enterprise stakeholders, translating technical vision into clear business value. - Build From Scratch: Engage in true greenfield development, focusing on execution, speed, and practical, production-ready solutions over unnecessary over-engineering. Qualifications - Deep Engineering Roots: A strong, foundational background in core engineering principles. You must have a proven track record as a heavy-hitting engineer prior to the mainstream adoption of AI coding tools. - Advanced AI Workflows: You can articulately demonstrate exactly how AI has transformed your development process. We want to hear how you use AI for architectural planning, reviewing diverse technical approaches, research, and writing implementation plans—not just as a basic autocomplete tool. - Tech Stack Agnostic (With Strong Anchors): The core ecosystem for these initial enterprise initiatives leverages Azure, .NET/C#, and React. While an exact match is ideal, we value strong grounding in any robust backend technology, paired with a willingness to adapt. - Sharp, Independent Thinkers: You must be able to hold your own in deep technical conversations, think critically on your feet, and communicate crystal-clear concepts without relying on external aids or scripts. Benefits - 100% Remote (US-Based) - Mid-$150k – $170k base (Bonus Eligible) - Enterprise hardware provided Company Description This is the ideal home for a builder who loves shipping real software fast, thrives in autonomy, and wants to work on massive enterprise modernization without the burden of maintaining archaic legacy code. You will have a direct hand in shaping how AI-driven engineering is executed at scale.
Founded in 1979, Cotiviti provides analytics-driven payment and network solutions for the healthcare and retail industries, offering services that help payers,
• The Generative AI Developer Intern focuses on researching and developing advanced healthcare informatics, particularly in the realm of Generative AI and emerging Artificial General Intelligence. • The intern will assist and take a primary role in developing, researching, creating analytical materials, and collaborating with various teams to drive AI-related projects. • Gain hands-on experience in generative AI model development, architecture design, and database management. • Communicates technology-related updates and requirements to other departments and contributes to presentations for senior management. • Collaborates with research and development teams, product management, and strategic analysts to support ongoing projects. • Partners with Business Units to provide reliable intelligence, validated technology options, and insights on enterprise and industry trends. • Supports technology project teams by coordinating specific tasks, assisting with day-to-day operations, and contributing to the successful delivery of solutions. • Completes all responsibilities and goals outlined in the internship program. • Completes all special projects and other duties as assigned.
Role Description - Define and execute the organization's enterprise AI vision, strategy, and operating model. - Develop a multi-year roadmap for AI adoption, platform investments, business enablement, and technology modernization. - Establish governance, funding, prioritization, and success metrics for AI initiatives. - Build and manage a balanced portfolio of AI programs that drive business value while maintaining appropriate operational, security, and risk controls. - Evaluate emerging technologies and identify opportunities to improve business performance, customer experiences, operational efficiency, and employee productivity. - Serve as a trusted advisor to executive leadership on AI strategy, opportunities, risks, and industry trends. - Lead the development of enterprise AI platform capabilities that support scalable, secure, and reusable AI solutions. - Define architectural standards for intelligent applications, automation platforms, digital assistants, enterprise knowledge solutions, and AI-enabled workflows. - Establish foundational capabilities supporting: - Model access and orchestration - Knowledge retrieval and search - Enterprise data integration - Monitoring and observability - Security and governance controls - Drive adoption of AI-assisted software development practices across the software delivery lifecycle. - Partner with architecture, engineering, infrastructure, operations, security, and data teams to modernize technology delivery through AI-enabled capabilities. - Collaborate with business leaders to identify and prioritize high-value AI use cases. - Lead initiatives focused on: - Intelligent automation - Decision support systems - Document and knowledge management - Workflow optimization - Productivity enhancement - Customer and employee experience improvements - Develop frameworks for evaluating, prioritizing, and scaling AI opportunities across the organization. - Establish adoption strategies, change management programs, and success measurements for enterprise AI initiatives. - Build organizational AI literacy through education, enablement programs, and communities of practice. - Partner with risk, security, legal, compliance, and data governance stakeholders to ensure responsible AI adoption. - Embed security, privacy, transparency, human oversight, and operational controls into AI platforms and business solutions. - Support the development and operationalization of enterprise AI governance frameworks. - Balance innovation and experimentation with appropriate governance and risk management practices. - Promote ethical and responsible use of AI technologies across the organization. - Lead delivery of enterprise AI initiatives from strategy through implementation and adoption. - Establish delivery frameworks, operating models, and performance measures that ensure successful execution. - Manage technology investments, budgets, vendor relationships, and strategic partnerships. - Drive accountability for scope, timelines, quality, adoption, and business outcomes. - Ensure AI initiatives are scalable, supportable, and aligned with enterprise architecture and operational standards. - Build and lead a high-performing team of AI, engineering, architecture, automation, and technology professionals. - Foster a culture of innovation, experimentation, accountability, and continuous learning. - Mentor leaders and technical teams while establishing clear goals, priorities, and performance expectations. - Serve as a catalyst for organizational change and technology transformation. - Promote collaboration across business and technology functions to accelerate enterprise adoption. Qualifications - 15+ years of progressive leadership experience in technology, digital transformation, software engineering, architecture, data, automation, or emerging technologies. - Significant experience leading enterprise-scale AI, automation, analytics, or digital transformation initiatives. - Demonstrated success building and executing technology strategies that deliver measurable business outcomes. - Experience operating within highly regulated or complex enterprise environments preferred. - Strong understanding of modern AI and Generative AI technologies, including: - Large language models and foundation models - AI orchestration and workflow automation - Retrieval and knowledge-based architectures - Prompt engineering and evaluation methodologies - AI application development patterns - Experience with intelligent automation, AI-enabled software development, and enterprise AI platforms. - Familiarity with responsible AI principles, governance frameworks, model lifecycle management, and operational monitoring. - Strong technical foundation across cloud platforms, APIs, distributed systems, data architecture, cybersecurity, identity, and modern engineering practices. - Proven ability to align technology investments with strategic business objectives. - Strong executive communication and stakeholder management skills. - Experience influencing senior leadership and driving enterprise-wide change initiatives. - Demonstrated success managing teams, vendors, consulting partners, and complex delivery portfolios. - Strong financial acumen, including budgeting, investment planning, vendor management, and value realization. Education - Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline required. - Advanced degree preferred. Leadership Characteristics - Strategic thinker with a passion for innovation and emerging technologies. - Hands-on leader who balances vision with execution. - Strong business acumen combined with deep technical credibility. - Collaborative influencer who builds alignment across diverse stakeholder groups. - Results-oriented operator focused on measurable outcomes and sustainable transformation. - Coach and mentor committed to developing talent and building high-performing teams.
• Design, develop, and deploy AI/ML solutions to modernize core pharmacy platforms, with a focus on scalability, reliability, performance, and security • Leverage Generative AI, LLMs, and agentic AI frameworks to automate and enhance pharmacy workflows and decision-making processes • Collaborate with business and technical stakeholders to understand pharmacy domain requirements and translate them into robust AI/ML-driven technical solutions • Contribute to architecture and technical design of AI/ML pipelines, including model selection, data integration, and deployment patterns • Establish and maintain AI/ML engineering standards, best practices, and quality assurance processes • Conduct code reviews, provide constructive feedback, and mentor engineers on modern AI/ML engineering practices • Partner with cross-functional teams including data scientists, product managers, platform engineers, and pharmacy domain experts • Translate legacy system modernization needs into scalable AI applications that enhance products, workflows, and operational efficiency • Monitor and optimize AI/ML model performance, resource utilization, and platform reliability • Collaborate with DevOps and infrastructure teams to ensure secure, scalable deployment and operations of AI/ML solutions • Stay current with advancements in AI/ML frameworks, Generative AI, LLMs, and pharmacy technology modernization
Founded in 2004 and led by CEO Steve Chapman, Natera is a company in the biotechnology market that offers genetic testing and diagnostics on a global scale. Ope
Senior AI-Machine Learning Engineer US Remote Role Overview The Senior AI/ML Engineer is responsible for designing, building, and deploying Natera’s Generative AI and Machine Learning platforms. The role needs excellent hands-on engineering excellence to build robust, compliant, and efficient Generative AI and ML platform components. This role requires deep expertise in Generative AI and machine learning engineering at scale, with a passion for building robust, compliant, and high-performance systems that directly impact patient outcomes and clinical innovation. You will design, build, and scale enterprise-grade AI/ML systems that power internal workflows (R&D, Lab Ops, Clinical Trials, Billing, Patient/Provider engagement) and external-facing AI/ML platforms. You will design and build cutting-edge AI solutions leveraging agentic architecture, retrieval-augmented generation (RAG), vector search, feature stores, LLMOps, experimentation, observability, and compliance-first AI pipelines. You will be responsible for development of a production-ready Generative AI and MLOps platform with reusable components used to deploy multiple AI solutions across Natera’s business units. You will also develop clear standards and best practices established for AI/ML development across the organization. Key Responsibilities Platform Development - Design and implement foundational GenAI services: vector search, prompt tuning, agent orchestration, document extraction, context/memory services, model/endpoint registry, feature/embedding stores, guardrails, and evaluation pipelines - Build the underlying infrastructure for autonomous and semi-autonomous AI agents including support for agent collaboration, reasoning, and memory persistence, enabling continuous context-aware execution - Build standardized APIs/SDKs that make it easy for product teams to compose, deploy, and monitor Generative AI workloads. - Ensure platform components meet enterprise-grade requirements for scalability, latency, multi-region resilience, and cost efficiency Generative AI Enablement - Stand up LLM runtimes with token/rate governance, caching, and safe tool-use - Implement RAG at scale: ingestion pipelines, chunking/embedding policies, hybrid search, relevance/risk scoring, and feedback loops - Build agent orchestration (single & multi-agent) with planning, tool routing, shared/persistent memory, and inter-agent communication - Integrate tooling and APIs that allow agents to interact with internal systems, retrieve data securely, and take action under strict controls - Collaborate with research teams to prototype and productionize multi-agent architectures for workflow automation, report generation, and data synthesis. Infrastructure & Automation - Implement cloud-native infrastructure for large-scale model training and serving using Kubernetes, MLflow, Terraform, and AWS-native services - Automate data and model pipelines for RAG, LLM fine-tuning, and agent orchestration - Integrate observability tools (Datadog or equivalent) for real-time performance, drift detection and safety monitoring of AI outputs - Optimize compute and storage architecture to ensure cost-effective scaling of large models and multi-agent workloads - Partner with security, data governance, SRE, and application teams to productize platform capabilities Safety, Security & Compliance Integration - Embed compliance-by-design (HIPAA/CLIA/CAP/FDA/GDPR): PHI/PII handling, encryption, access controls, audit trails - Implement guardrails: input/output filters, prompt hardening, allow/deny policies for tool execution, policy-as-code in CI/CD - Bias/explainability hooks and automated evaluations for RAG/LLM/agents; drift and regression detection Technical Leadership & Mentorship - Establish golden paths (templates, examples, docs) and lead platform architecture reviews, code reviews, and design discussions - Partner with data scientists, AI researchers, and product engineers to deliver reliable and maintainable AI services - Mentor junior engineers in platform development, distributed systems, and agentic AI infrastructure concepts - Influence cross-functional roadmaps by partnering with Product and Engineering leadership to align delivery with business needs Qualifications Required: - 8+ years in software/ML engineering, with 5+ years in ML engineering at scale - Expertise in building production-grade ML/LLM systems on AWS tech stack (Python, TensorFlow/PyTorch, Spark, MLflow/Kubeflow, vector DBs) - Proven track record with GenAI/LLMs: fine-tuning, RAG, prompt orchestration, agentic systems, safety guardrails, monitoring, and cost optimization - Hands-on with RAG systems (embeddings, vector DBs, retrieval policies) and LLM runtime operations (caching, quotas, multi-model routing) - Experience building agentic AI platforms (LangChain, LlamaIndex, CrewAI, Semantic Kernel, or custom) - Deep knowledge of data-intensive systems, distributed architectures, and cloud-native development - Strong grounding in compliance-first engineering in healthcare, biotech, or diagnostics preferred - Track record building secure, compliant data/AI systems and automating policy checks. - Excellent ability to influence across teams, mentor engineers, and set technical standards Preferred: - Masters degree in Computer Science, AI/ML, engineering or related field - Experience in healthcare, pharma, diagnostics, or other regulated industries - Familiarity with AI governance frameworks, bias detection, explainability, and compliance (e.g., HIPAA, CLIA, FDA) The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years & depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations. Remote USA $125,000 - $156,300 USD OUR OPPORTUNITY Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives. The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management. WHAT WE OFFER Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program! For more information, visit www.natera.com. Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide. All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws. If you are based in California, we encourage you to read this important information for California residents. Link: https://www.natera.com/notice-of-data-collection-california-residents/ Please be advised that Natera will reach out to candidates with a @natera.com email domain ONLY. Email communications from all other domain names are not from Natera or its employees and are fraudulent. Natera does not request interviews via text messages and does not ask for personal information until a candidate has engaged with the company and has spoken to a recruiter and the hiring team. Natera takes cyber crimes seriously, and will collaborate with law enforcement authorities to prosecute any related cyber crimes.
Role Description Buscamos um Machine Learning Engineer com perfil hands-on e visão de produto para atuar no ciclo completo de soluções de IA, com profundidade em: - Agentes inteligentes - Frameworks agênticos - Fine-tuning de LLMs - Treinamento e otimização de modelos clássicos de clusterização e aprendizado supervisionado - Deploy em ambientes produtivos com práticas modernas de MLOps e IA Generativa O profissional será responsável por: - Desenhar, treinar, customizar (fine-tuning) e operacionalizar modelos, agentes e pipelines multi-agente que impulsionem produtos e plataformas, com foco em escalabilidade, automação e integração em nuvem. - Assumir ownership das frentes sob sua responsabilidade. Responsibilities - Projetar e desenvolver agentes inteligentes e sistemas multi-agente ponta a ponta, incluindo orquestração, memória, ferramentas (tool use), guardrails e protocolos de comunicação (MCP, A2A). - Fine-tuning de Modelos (RL, LoRA/QLoRA) e técnicas de adaptação como distillation e prompt optimization. - Implementar e evoluir pipelines de RAG, Graph, RAG e recuperação semântica com embeddings vetoriais e bancos vetoriais (pgvector, Qdrant, Weaviate, etc.). - Projetar, treinar e validar modelos de Machine Learning e Clusterização (ex: K-Means, K-Medoids, DBSCAN, GMM, embeddings vetoriais). - Realizar deploy e monitoramento de modelos e agentes em ambiente produtivo (Cloud Run, Vertex AI, API endpoints). - Implementar pipelines de MLOps e LLMOps, com versionamento, CI/CD, testes automatizados, rastreabilidade e avaliação contínua (MLFlow, Kubeflow, Vertex Pipelines, LangSmith, Langfuse). - Desenvolver e integrar agentes e fluxos de automação utilizando ADK (Agent Developer Kit), LangGraph, CrewAI, AutoGen, N8N ou frameworks correlatos de orquestração agentic. - Estruturar frameworks de avaliação para agentes e LLMs (LLM-as-Judge, eval sets, benchmarks customizados, métricas de qualidade e alinhamento). - Identificar problemas e propor approaches técnicos antes de serem demandados, defendendo decisões de arquitetura com base em evidência técnica e validando hipóteses rapidamente. - Atuar como referência técnica de IA para o time e stakeholders, conduzindo discussões de arquitetura, code reviews críticos e mentorias para pares de engenharia e produto. - Conduzir frentes em paralelo e destravar dependências ativamente, comunicando blockers cedo e propondo caminhos alternativos quando uma frente trava. - Criar e manter documentação técnica e artefatos de engenharia, garantindo boas práticas de governança e reprodutibilidade. - Apoiar times de produto e engenharia na integração de modelos e agentes em aplicações (APIs, microsserviços, containers). - Avaliar continuamente novas abordagens de IA Generativa, arquiteturas agênticas e integração de LLMs. Qualifications - Formação em Ciência da Computação, Engenharia, Estatística, Matemática Aplicada ou áreas correlatas. - Sólido conhecimento em Python e bibliotecas de ML/AI (Scikit-Learn, PyTorch, TensorFlow, Hugging Face Transformers). - Experiência prática construindo agentes em produção com ao menos um framework agentic (ADK, LangGraph, CrewAI, AutoGen, LlamaIndex Agents ou similares). - Experiência com fine-tuning de LLM incluindo preparação de datasets, treinamento e avaliação. - Domínio de conceitos de vetorização, embeddings e busca semântica (Sentence Transformers, BERT, modelos de embedding modernos) — incluindo opinião técnica formada sobre escolha de banco vetorial e estratégias de chunking/retrieval. - Conhecimento prático de deploys em nuvem (Cloud Run, Vertex AI, SageMaker, etc.). - Vivência com ferramentas de versionamento e rastreabilidade de modelos e prompts (MLFlow, Kubeflow, Vertex Pipelines, Phoenix, LangSmith, Langfuse). - Experiência em integração via APIs e construção de microsserviços. - Entendimento de boas práticas de MLOps e LLMOps: monitoramento, logging, rollback, retraining, eval automatizado, observabilidade de agentes. - Familiaridade com MCP (Model Context Protocol) e padrões modernos de integração entre LLMs e ferramentas externas. - Ownership ponta-a-ponta de iniciativas de IA — capacidade de conduzir uma frente do problema à entrega sem necessidade de microgestão. - Uso fluente de ferramentas de produtividade técnica (Claude Code, Cursor, Copilot, agentes de coding) para acelerar entrega e destravar decisões. - Comunicação técnica proativa: comunica blockers, trade-offs e mudanças de direção cedo e por escrito, sem precisar ser provocado. - Postura proativa na identificação de problemas — capacidade de antecipar o que precisa ser feito antes de ser demandado, em vez de aguardar atribuição. - Convicção técnica baseada em evidência — defende approaches arquiteturais com clareza e revisita decisões apenas mediante dado novo que justifique a mudança. - Domínio técnico consolidado do estado da arte — chega em discussões de arquitetura com posição formada sobre decisões fundamentais do domínio (escolha de banco vetorial, fine-tuning vs. RAG, framework de eval, etc.). - Atuação como sparring técnico para liderança e pares de engenharia, sendo referência ativa em decisões de IA — e não o contrário. - Uso fluente de ferramentas modernas de produtividade técnica (Claude Code, Cursor, Copilot, agentes de coding) para destravar a si e ao time. Requirements - Experiência com arquiteturas agênticas avançadas: multi-agent debate, DAGs de agentes. - Defende approaches técnicos com convicção e evidência. - Experiência comprovada em GCP e/ou AWS (Vertex AI, BigQuery, Cloud Functions, Bedrock, SageMaker). - Deploys de modelos e agentes via Cloud Run, Vertex Endpoints ou Bedrock Agents. - Experiência com modelos open-source self-hosted (Llama, Qwen, DeepSeek, Mistral) e infraestrutura de inference (vLLM, TGI, Ollama). - Pipelines de geração e curadoria de datasets sintéticos para fine-tuning. - Experiência prévia liderando ou influenciando tecnicamente squads de IA/ML. - Certificações em IA, Cloud (GCP ML Engineer, AWS ML Specialty) ou áreas correlatas. Benefits - Política de Equipamentos: Bring Your Own Device (BYOD). - Seguem o calendário nacional de feriados no Brasil. Todos os feriados e emendas são respeitados. - Férias coletivas de Natal e Ano Novo +1 semana de férias a ser escolhida durante os 12 meses de contrato.
Role Description AI Architect is a technical leader reporting to the Director, Enterprise App Engineering. This role defines and drives Guidewire's enterprise AI strategy — translating business objectives across GTM, Finance, HR, Legal, and Engineering into production-grade AI systems that deliver measurable impact. The architect owns end-to-end design across ingestion, orchestration, agent frameworks, security, compliance, and delivery. - Define and own Guidewire's enterprise AI architecture vision — establishing technical direction, design standards, and reference implementations across all BizTech AI initiatives. - Architect and lead delivery of production AI systems: multi-agent orchestration platforms, RAG and GraphRAG knowledge systems, LLM-powered workflows, and enterprise context graph initiatives spanning all Systems of Record. - Own the full-stack AI architecture — from AWS infrastructure and data pipelines (Kafka, Iceberg, dbt, pgvector) through LLM gateway design, agent orchestration (LangGraph or equivalent), and user interfaces. - Help define enterprise AI security and governance standards — covering IAM, ABAC policy enforcement, PII handling, prompt injection defence, hallucination detection, and audit trail design for SOX and GDPR compliance. - Lead complex, multi-team AI programs end to end — from stakeholder alignment and architectural design through delivery, adoption, and measured business outcomes across concurrent initiatives. - Help drive technology evaluation and build-vs-buy decisions across the AI tooling portfolio. - Partner with Data Governance, Security, Legal, and Compliance teams to embed AI responsibly across the organisation. - Mentor senior engineers and foster a culture of technical excellence, pragmatic innovation, and accountable delivery across the BizTech organization. Qualifications - Bachelor's Degree in Computer Science, Engineering, or equivalent work experience. - 12+ years in enterprise software architecture, with at least 2 years designing and delivering production AI/ML or LLM-based systems at scale. - Proven track record leading large, complex enterprise AI programs — multi-year, multi-team initiatives with clear, measurable business outcomes. - AWS expertise: Bedrock, EKS, Lambda, RDS, S3, Kinesis, IAM, VPC. AWS Solutions. - Full-stack AI fluency: event ingestion and streaming pipelines, vector and graph databases, LLM orchestration and agent frameworks, API design, and front-end delivery. - Experience with AI observability and evaluation — trace instrumentation, eval frameworks, and production monitoring (Datadog LLM Observability, LangSmith, or equivalent). - iPaaS and integration architecture experience — Workato, MuleSoft, or equivalent for enterprise SoR integration and event-driven automation. - 5+ years of technical leadership — mentoring senior engineers, driving architecture strategy, and managing cross-functional stakeholder relationships. - Excellent communication and executive presence — able to present complex AI architecture decisions clearly to both engineering teams and senior business leadership. - Demonstrated experience shipping Generative AI systems in production enterprise environments, with measurable impact on business outcomes. Requirements - The US base salary range for this full-time position is $132,000 - $198,000. Your base pay will depend on your experience, skills, education, training, and location among other factors. - All full-time positions or part-time roles working 30 hours or more a week at Guidewire are eligible for benefits that support their health and well-being including health, dental, and vision insurance, paid time off, and a company sponsored retirement plan. - In addition, some roles may be eligible for the annual company bonus plan, commissions, and/or long term incentive awards which are contingent on a variety of factors including, but not limited to, company and employee performance. Benefits - Disability Accommodations and Guidewire’s Appeals Process. Guidewire provides accommodations to the hiring process to create a fair opportunity for candidates with disabilities to contend for open positions. - Accommodation requests should be directed to Accommodations@guidewire.com. - If things do not go as hoped, we invite you to use our appeals process. Guidewire promises to independently review any denied accommodation and any decision not to offer you the position. - The appeals process is the same in either case. Within five business days of receiving a notice of denial of an accommodation, or receiving a notice of your non-selection for a vacancy, e-mail Accommodations@guidewire.com to make an appeal. - Guidewire will assign a new decision-maker to review the request and/or hiring decision, who will then notify you in writing of a decision within 10 business days.
A venture-backed startup building a modern data platform for the real estate industry, enabling automation, analytics, and AI-powered workflows for real estate operators. The team includes engineers and leaders from companies such as major fintech, cloud, and consumer technology platforms, and is focused on solving complex infrastructure and data challenges in a large, underserved industry.
Role Description A fast-growing cloud-native professional services company is hiring Forward Deployed Engineers for a new AI-focused business unit built around enterprise LLM adoption. This is a startup-style team inside a larger, established consulting organization: small team, high autonomy, direct client ownership, and real delivery responsibility from day one. You’ll work directly with enterprise customers to take AI from idea to production. Engagements may focus on AI-assisted software development, agentic engineering workflows, RAG/LLM applications, or applied AI enablement for business teams. - Own client engagements end-to-end: discovery, scoping, architecture, delivery, enablement, and expansion. - Run stakeholder interviews, technical workshops, architecture reviews, and enablement sessions. - Build or guide production AI systems such as agents, RAG pipelines, AI-native apps, workflow automations, or AI-assisted SDLC tooling. - Translate ambiguous business problems into practical, deliverable technical solutions. - Work with both technical and non-technical stakeholders, from engineers and architects to VP/C-suite leaders. - Identify opportunities to expand accounts through strong delivery and proactive problem-solving. - Leave clients with durable capability: documented, maintainable systems, workflows, and enablement materials. Qualifications - 4–10 years of experience in client-facing technical delivery, consulting, systems integration, professional services, FDE, solutions engineering, or customer-facing software engineering. - Hands-on experience with LLM applications, AI agents, RAG pipelines, AI workflows, or AI-assisted development tools. - Comfortable with tools such as Claude, Claude Code, Cursor, Codex, AWS Bedrock, LangChain/LangSmith, Python, AWS, Kubernetes, Zapier, Power Automate, or similar. - Strong consulting instincts: able to run discovery, manage stakeholders, communicate clearly, and drive outcomes without heavy oversight. - Able to speak technically with engineers while also explaining business value to executives. - High tolerance for ambiguity and a bias toward action. Requirements - Two strong-fit profiles: - AI/Workflow Transformation FDE: Consulting, implementation, or enterprise SaaS delivery background with hands-on AI enablement experience. Strong fit for candidates who have helped business teams adopt AI tools, automate workflows, or transform knowledge-worker processes. - Technical / SDLC FDE: Software engineering background with client-facing delivery experience. Strong fit for candidates who have built production AI systems, agentic coding workflows, developer tooling, RAG apps, or engineering enablement programs. - Strong signals: - Prior consulting, professional services, systems integration, or implementation experience. - Experience leading workshops, enablement sessions, or technical discovery. - AWS/cloud consulting background. - Production AI/LLM project experience. - Account expansion or “sell-do” delivery experience. - Comfort working remotely with up to 50% travel depending on client needs.
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