AI Engineer

Oakwood Systems Group Inc. seeks an experienced, hands-on AI Engineer to deliver secure, production-ready generative AI and agentic solutions on the Microsoft cloud.

Responsibilities

• Design, build, and deploy production AI applications, copilots, and agents using Microsoft Azure and modern AI frameworks.
• Build RAG and enterprise knowledge solutions using embeddings, vector search, document processing, metadata, and retrieval strategies for grounded, source-aware answers.
• Design agentic workflows using tools, APIs, structured outputs, memory/state, human approval, and multi-step orchestration to automate business processes safely.
• Integrate AI capabilities with enterprise applications and data sources via REST APIs, Microsoft Graph, Power Platform, databases, SaaS platforms, and custom services.
• Develop maintainable backend services and APIs, primarily in Python, including testing, error handling, logging, security, and performance considerations.
• Define and apply evaluation approaches for AI quality, groundedness, reliability, safety, latency, and cost; use results to improve prompts, retrieval, orchestration, and model selection.
• Apply production engineering practices: source control, automated testing, CI/CD, containerization, secrets management, observability, and environment promotion.
• Implement enterprise security and governance practices, including identity, role-based access, data boundaries, auditability, responsible AI controls, and human-in-the-loop patterns where appropriate.
• Partner with client business and technical stakeholders to clarify requirements, prototype solutions, explain tradeoffs, and translate AI possibilities into deployable designs.
• Support new client opportunities through architecture reviews, code reviews, estimation, technical discovery, and solution design.
• Create reusable patterns, accelerators, reference implementations, and delivery assets to improve speed and consistency across engagements.
• Document solutions and support knowledge transfer so client teams can operate and extend delivered capabilities.
• Mentor less-experienced engineers and help establish pragmatic engineering standards for production AI delivery.

Requirements

• 5+ years professional engineering experience across software engineering, machine learning engineering, data engineering, applied AI, backend development, or a closely related discipline, with meaningful ownership of production systems.
• 2+ years hands-on experience building generative AI / LLM-powered applications or AI-enabled software, including at least one solution moved beyond prototype into a production or production-like environment.
• Strong Python skills and experience building production services, APIs, integrations, or data/AI pipelines.
• Hands-on experience with LLM application patterns: prompt design, RAG, embeddings, vector search, structured outputs, function/tool calling, and agent orchestration.
• Experience evaluating AI systems using measurable criteria (not only subjective demos), including quality, reliability, safety, latency, and/or cost.
• Cloud-based application experience with strong Azure experience preferred; ability to work effectively in a Microsoft-centric environment.
• Working knowledge of SQL and enterprise data integration, including reasoning about data quality, access, lineage, and how data architecture impacts AI outcomes.
• Modern engineering practices: Git, peer review, automated testing, CI/CD, containers, APIs, logging/monitoring, and secure configuration/secrets management.
• Ability to troubleshoot production AI behavior including retrieval failures, hallucinations, tool-calling errors, prompt regressions, latency, cost, and edge cases.
• Strong client-facing communication to explain technical concepts, architecture decisions, risks, and tradeoffs to engineers and business stakeholders.
• Ability to work independently in a consulting environment, manage competing priorities, and move from incomplete requirements to a well-structured implementation plan.
• Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent practical experience.

Technologies

• Microsoft Azure, Azure AI Search, Azure OpenAI, Azure AI Foundry
• Python, embeddings, vector search, document processing, structured outputs
• Function/tool calling, agent orchestration (Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI)
• REST APIs, Microsoft Graph, Power Platform, Power Automate
• Databases, SQL
• Git, CI/CD, containers, logging/monitoring, secrets management, observability
• Identity, role-based access, human-in-the-loop, Azure Functions, Container Apps, App Service, AKS, API Management
• Cosmos DB, Azure SQL, Key Vault, Entra ID
• MLOps, LLMOps, Microsoft Fabric, Databricks
• Multimodal models, fine-tuning, model serving
• GitHub Copilot, Claude Code, Codex, Cursor, Model Context Protocol (MCP)
• Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI, Model Context Protocol (MCP)
• Microsoft Fabric

Preferred Qualifications

• Hands-on experience with Microsoft Foundry / Azure AI Foundry, Azure OpenAI, Azure AI Search, Copilot Studio, Power Automate, or related Microsoft AI services.
• Experience with orchestration frameworks such as Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI, or custom agent frameworks.
• Experience with Model Context Protocol (MCP), agent-to-tool integration patterns, or reusable enterprise tool registries.
• Experience with Azure application services including Functions, Container Apps, App Service, AKS, API Management, Cosmos DB, Azure SQL, Key Vault, and Entra ID.
• Experience with MLOps / LLMOps practices such as evaluation pipelines, model or prompt versioning, tracing, monitoring, feedback loops, and production incident response.
• Experience with Microsoft Fabric, Databricks, or other modern data platforms for governed enterprise data preparation for AI.
• Experience with classical machine learning, NLP, deep learning, fine-tuning, multimodal models, or model serving when foundation-model APIs are not sufficient.
• Experience using AI-assisted development tools (GitHub Copilot, Claude Code, Codex, Cursor) with disciplined engineering review for correctness, security, and maintainability.
• Prior consulting, professional services, systems integration, or Microsoft partner experience.
• Relevant Microsoft or cloud certifications (Azure AI Engineer, Azure Developer, Azure Solutions Architect, or equivalent).

What Success Looks Like

• AI solutions progress from discovery and prototype to secure, supportable production deployments with clear acceptance criteria.
• Clients can articulate business value beyond technical novelty.
• AI quality improves through repeatable evaluation, observability, and feedback rather than one-time demonstrations.
• Solutions integrate with client applications, data, identity, and operating processes and remain maintainable after handoff.
• Reusable engineering patterns and delivery assets reduce effort and risk on future engagements.
• Trusted delivery that combines technical depth, sound judgment, and practical execution.

Why Oakwood

• Deep Microsoft expertise combined with hands-on application, data, cloud, and AI engineering.
• Microsoft Solutions Partner designations across key solution areas and an Advanced Specialization for AI Applications on Microsoft Azure.
• AI Engineers work directly with clients, build production solutions, shape reusable delivery patterns, and help organizations turn rapidly evolving AI capabilities into durable business outcomes.

Location and Experience

• Location: St. Louis, MO (onsite)
• Experience: 5+ years