Senior AI Engineer (Agentic Systems / LLMs / Python) · LATAM - Location: Anywhere in LATAM - Job Type: Remote - Project: Fintech Conversational AI Platform - Time Zone: Flexible within LATAM - English Level: B2 / C1 _______________________________________________ Disclaimer – Must read: Commitment & Focus This role requires full-time dedication, with clear priority given to Darwoft projects during the established working hours.It is not compatible with other full-time professional engagements.Any additional professional activities must be disclosed in advance and must not interfere with the responsibilities or working hours of this role. Get to Know Us At Darwoft, we create software that drives real change. But we're more than just tech — we're people first. We believe in building human-centered digital experiences and having fun while we do it. Our team is curious, passionate, and always growing. Based in Latin America, we partner with companies worldwide to develop innovative solutions with purpose. We work remotely, collaboratively, and with a strong sense of community — always embracing continuous learning, adaptability, and real impact. About the Role We're looking for a Senior AI Engineer to join a high-impact fintech project focused on building next-generation conversational AI systems. This is not a traditional chatbot role. You'll be working on production-grade agentic systems, evolving AI from simple interactions into autonomous, multi-agent architectures capable of reasoning, planning, and executing complex workflows across critical business domains. You'll operate close to the core AI strategy, collaborating with product and engineering teams to bring intelligent systems into real-world production environments — with direct impact on thousands of users. What You'll Be Doing - Design, build, and deploy stateful AI agents using Python and modern agentic frameworks (LangGraph, CrewAI, etc.) - Develop multi-agent systems capable of handling complex, multi-step workflows with reasoning and planning - Implement and optimize RAG pipelines using vector databases for accurate and grounded outputs - Integrate AI agents into core product infrastructure (APIs, internal services, business workflows) - Build LLMOps capabilities, including monitoring, tracing, and observability of agent behavior (reasoning paths, latency, tool usage) - Design advanced evaluation pipelines (evals-as-code) using techniques like LLM-as-a-judge, semantic similarity, and adversarial testing - Optimize systems for performance and cost efficiency (prompt optimization, caching, model routing) - Ensure production readiness through CI/CD pipelines, containerization (Docker/Kubernetes), and system reliability practices - Collaborate with cross-functional teams to translate business needs into scalable AI solutions - Mentor and contribute to best practices in AI engineering and software craftsmanship What You Bring - 7+ years of experience in software engineering - 2+ years building AI / Generative AI systems in production - Strong proficiency in Python - Hands-on experience with LLM frameworks and APIs (OpenAI, LangChain, LlamaIndex, CrewAI, or similar) - Proven experience designing agentic systems (multi-agent workflows, memory/state management) - Solid experience with RAG architectures and vector databases - Strong understanding of software engineering fundamentals (Git, testing, CI/CD) - Experience integrating AI into real production environments, not just prototypes - Ability to translate business problems into scalable technical solutions - Strong communication skills in a remote, English-speaking environment Nice to Have - Experience in fintech, payments, fraud, or financial platforms - Experience with LLMOps tools (LangSmith, Arize, Weights & Biases, etc.) - Experience with evaluation frameworks and benchmarking for LLM systems - Background in cost optimization of AI systems at scale - Contributions to open-source or public AI projects - Experience working in high-growth, fast-paced environments What Darwoft Offers - Contractor agreement with payment in USD - 100% remote work - Argentina's public holidays - English classes - Referral program - Access to learning platforms Explore this and other opportunities at: www.darwoft.com/careers

About the role This is not a role where you will bolt AI onto an existing product as an afterthought. SqlDBM is rebuilding core workflows around AI — from how data architects design schemas to how engineers validate changes to how governance teams maintain compliance across enterprise environments. You will work at the intersection of .NET backend engineering and applied AI — building the systems that make AI a first-class, reliable, enterprise-grade capability inside one of the most technically demanding categories in data infrastructure. This role sits inside our dedicated AI group and reports directly to senior leadership. You will have a short line to product, architecture, and business decisions — no layers, no queue. The team is small by design, moves fast, and has direct visibility into enterprise customer needs. What makes this role different You are not building a chatbot. You are building AI infrastructure for enterprise data teams — systems that understand schema context, generate governed artifacts, and integrate into the workflows of the world's most sophisticated engineering organizations. The problems are hard. The customers are demanding. The work is meaningful. What you'll do You will own and extend the backend systems that power SqlDBM's AI capabilities. Specific areas include: - AI-assisted data modeling workflows — backend services that allow users to describe their data needs in natural language and receive accurate, governed schema output - Context-aware intelligence — systems that use the full richness of a user's data model, naming standards, and governance rules to produce AI output that is specific to their environment, not generic - Automated documentation and metadata generation — AI pipelines that analyze existing schemas and produce accurate, consistent business documentation at scale - Integration with enterprise AI ecosystems — API layers that allow external AI agents and tools to call SqlDBM as a trusted source of schema context - Consumption tracking and orchestration — backend infrastructure that manages AI request routing, model selection, cost optimization, and usage metering - Governance-aware AI workflows — systems that embed approval, validation, and compliance logic into AI-generated outputs before they reach production - MCP server development — building and extending SqlDBM's Model Context Protocol server so that external AI agents and LLM-based tools can use SqlDBM as a trusted, real-time schema authority - Tech Stack includes: C# / .NET AWS PostgreSQL Redis REST APIs LLM Integration MCP Microservices Our backend is built on modern .NET with a microservices architecture deployed on AWS. AI capabilities are built on top of major LLM providers via API, with proprietary prompt engineering, context management, and output validation layers that are core intellectual property of the platform. Qualifications - 5+ years of backend engineering experience with C# and .NET - Strong understanding of REST API design and asynchronous service architectures - Experience integrating with external APIs and managing complex data pipelines - Comfort working with LLMs via API — understanding of prompt construction, context management, token economics, and output validation - Experience building systems that handle variable, structured data — schemas, metadata, or similar - Strong engineering fundamentals — testing, code review, system design, observability - Ability to work independently in a remote-first, fast-moving engineering team Strong Plus - Experience with data modeling, database design, or data engineering tooling - Familiarity with enterprise data platforms — Snowflake, Databricks, dbt, or similar - Background building developer tools or platforms used by technical teams - Experience with agentic AI workflows, tool-use patterns, or AI infrastructure - Knowledge of semantic layer concepts, ontologies, or structured metadata systems - Familiarity with Model Context Protocol (MCP) — building or consuming MCP servers in agentic AI architectures What We Offer - Competitive base salary and equity in a profitable, growing company - Fully remote — work from anywhere - Direct impact on product direction — small team, no layers, your work ships to enterprise customers - Work on genuinely hard technical problems at the frontier of AI and enterprise data infrastructure - Collaborative, engineering-driven culture that moves fast and trusts its people - Benefits - comprehensive insurance coverage for employees and their dependents — including medical, dental, vision, life, and both short- and long-term disability, parental leave, an employer-sponsored 401(k) retirement plan, and stock options. Why now SqlDBM is at an inflection point. We have enterprise customers, a profitable business, and a product that is becoming something significantly more powerful. The engineers who join now will shape what that means and build the systems that define the next chapter of data architecture tooling.

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards. WHY JOIN US If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you! ABOUT THE ROLE We are looking for a Full Stack AI Software Engineer to build modern applications while shaping how AI enhances the software development lifecycle. In this role, you’ll design scalable systems across Python, Node.js, and React while defining best practices for AI-assisted coding, code analysis, and developer productivity. You’ll play a key role in driving engineering excellence by integrating LLM-powered tools into real-world development workflows. WHAT YOU WILL DO - Design, build, and maintain high-quality fullstack applications using modern technologies; - Write clean, scalable, and maintainable code across both frontend and backend systems; - Define and implement best practices for leveraging AI models to review, analyze, and improve code quality; - Use AI tools to enhance development workflows and engineering productivity; - Evaluate and implement AI-assisted development approaches across the engineering lifecycle; - Collaborate with other engineers to improve development standards, code reviews, and technical architecture; - Take ownership of technical solutions and drive engineering excellence across projects. MUST HAVES - 4+ years of experience in full-stack development with React and .NET / C#; - Proven experience building and shipping production-grade software systems; - Experience using AI tools such as OpenAI, Claude Code, or similar; - Proven experience with Azure; - Ability to define and implement best practices for integrating AI into the software development lifecycle; - Strong coding skills and software engineering fundamentals; - Ability to work independently and take ownership of complex technical initiatives; - Upper-intermediate English level. NICE TO HAVES - Additional experience working with AI/LLM-powered development tools or AI-assisted coding workflows; - Experience designing or integrating AI models or AI-driven development processes; - Background in AI/ML engineering, LLM applications, or AI-based developer tooling. PERKS AND BENEFITS - Professional growth: Mentorship, TechTalks, and personalized growth roadmaps. - Competitive compensation: USD-based pay with education, fitness, and team activity budgets. - Exciting projects: Modern solutions with Fortune 500 and top product companies. - Flextime: Flexible schedule with remote and office options.

AI Sr. Engineer – LLMOps & MLOps - 100% Remote Job Description This is a high-stakes, execution-focused role within the Transformation Office. We are looking for a "day-one" engineer to own the production lifecycle of our AI initiatives. Your mission is to build the automated infrastructure that bridges our legacy data systems with modern AWS and Azure AI services. You will be responsible for the "Ops" of AI: ensuring that LLM applications, RAG pipelines, and traditional ML models are deployable, observable, and scalable in a multi-cloud environment. Key Responsibilities • Multi-Cloud Pipeline Execution: Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio). • LLMOps Framework Implementation: Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. • Legacy Data Connectivity: Build the engineering "pipes" to securely ingest and move data from legacy systems (Mainframes, SQL Server, on-prem DBs) into cloud-native MLOps workflows. • Automated Model Evaluation: Implement systemized frameworks for LLM evaluation (LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation to ensure performance before deployment. • Observability & Monitoring: Deploy real-time monitoring for model drift, hallucination detection, latency, and token consumption to manage both quality and cost. • Infrastructure as Code (IaC): Manage all AI resources using Terraform or CloudFormation, ensuring the cloud posture is reproducible, secure, and follows a "Privacy by Design" mandate. • Advanced Analytics Integration: Partner with teams using platforms like Palantir, Databricks, or Snowflake to ensure a high-fidelity data flow between analytical ontologies and production models. • IT & Security Diplomacy: Work directly with central IT and Security to navigate IAM roles, VPC peering, and firewall configurations, clearing the path for rapid transformation. • Scalable Inference Engineering: Optimize model serving endpoints for high-throughput and low-latency, utilizing containerization (Docker/Kubernetes) and serverless architectures where appropriate. • Prompt & Model Versioning: Establish rigorous version control for prompts (PromptOps), model weights, and data snapshots to ensure 100% auditability and rollback capability. • Data Science Engineering: Support the data science lifecycle by automating feature stores, feature engineering pipelines, and the transition of experimental notebooks into hardened production microservices. • Security & Compliance Hardening: Implement automated scanning and guardrails (e.g., Bedrock Guardrails or Azure Content Safety) to prevent prompt injection and data leakage. - Qualifications • Education: Bachelor’s degree in Computer Science or a related field required; Master’s degree in a quantitative discipline highly desirable. • Proven Execution: 6+ years of engineering experience, with a minimum of 3 years strictly focused on MLOps or LLMOps in a production environment. • AWS & Azure Mastery: Deep, hands-on proficiency in both ecosystems. You must be able to configure Bedrock and Azure OpenAI services, including private networking and endpoint security, on day one. • Technical Stack: Expert Python, SQL, and PySpark. Extensive experience with containerization (Docker, Kubernetes) and orchestration tools (Airflow, Kubeflow, or Step Functions). • LLM Tooling: Professional experience with evaluation and observability frameworks like LangSmith, Arize Phoenix, or WhyLabs. • Data Science Flavor: A strong understanding of statistical validation, model evaluation metrics, and the ability to partner with Data Scientists to optimize model performance. • Transformation Mindset: The ability to move at the speed of a startup while maintaining the collaborative relationships required to function within a large-scale enterprise IT landscape.