Oakwood Systems Group Inc. brings deep Microsoft expertise together with hands-on engineering across application, data, cloud, and AI. As an AI Engineer, you will work directly with clients to design secure, production-ready generative AI and agentic solutions on the Microsoft cloud, with an emphasis on reusable delivery patterns and durable business outcomes. Oakwood holds Microsoft Solutions Partner designations across key solution areas and an Advanced Specialization for AI Applications on Microsoft Azure, giving you a strong foundation for real-world delivery.
What you’ll build and deliver
- Design, build, and deploy production AI applications, copilots, and agents using Microsoft Azure and modern AI frameworks.
- Create RAG and enterprise knowledge solutions using embeddings, vector search, document processing, metadata, and retrieval strategies that support grounded, source-aware answers.
- Develop agentic workflows that automate real business processes safely using tools, APIs, structured outputs, memory/state, human approval, and multi-step orchestration.
- Integrate AI capabilities with enterprise systems through REST APIs, Microsoft Graph, Power Platform, databases, SaaS platforms, and custom services.
- Implement maintainable backend services and APIs primarily in Python, with testing, error handling, logging, security, and performance considerations.
- Establish evaluation approaches for AI quality, groundedness, reliability, safety, latency, and cost, then use results to improve prompts, retrieval, orchestration, and model selection.
- Apply production engineering practices including source control, automated testing, CI/CD, containerization, secrets management, observability, and environment promotion.
- Apply enterprise security and governance practices, including identity, role-based access, data boundaries, auditability, responsible AI controls, and human-in-the-loop patterns where appropriate.
How you work with clients and teams
- Collaborate with client business and technical stakeholders to clarify requirements, prototype solutions, explain tradeoffs, and translate AI opportunities into deployable designs.
- Contribute to architecture reviews, code reviews, estimation, technical discovery, and solution design for new client opportunities.
- Create reusable patterns, accelerators, reference implementations, and delivery assets to improve consistency and speed across engagements.
- Document solutions clearly and support knowledge transfer so client teams can operate and extend what you deliver.
- Mentor less-experienced engineers and help establish pragmatic engineering standards for production AI delivery.
Qualifications
- 5+ years of professional experience in 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 that 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 such as prompt design, RAG, embeddings, vector search, structured outputs, function/tool calling, and agent orchestration.
- Experience evaluating AI systems using measurable criteria (quality, reliability, safety, latency, and/or cost) rather than subjective demos alone.
- Cloud experience with a strong preference for Azure and comfort working effectively in a Microsoft-centric environment.
- Working knowledge of SQL and enterprise data integration, including data quality, access, lineage, and how data architecture affects AI outcomes.
- Experience with 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 communication skills 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 structured implementation plan.
- Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent practical experience.
Technology focus
Microsoft Azure, Python, embeddings, vector search, document processing, structured outputs, function/tool calling, agent orchestration, REST APIs, Microsoft Graph, Power Platform, databases, SQL, Git, CI/CD, containers, logging/monitoring, secrets management, observability, identity, role-based access, human-in-the-loop, Azure AI Search, Azure OpenAI, Azure AI Foundry, Copilot Studio, Power Automate, Semantic Kernel, LangGraph, AutoGen, LangChain, CrewAI, Model Context Protocol (MCP), 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, and additional AI development tooling including GitHub Copilot, Claude Code, Codex, and Cursor.
Why Oakwood
- Work with clients to build real production solutions and help organizations translate rapidly evolving AI capabilities into lasting business results.
- Leverage Microsoft partner standing, including designations across key solution areas and an Advanced Specialization for AI Applications on Microsoft Azure.
- Build reusable delivery patterns and accelerators that improve consistency and reduce risk across future AI engagements.
Location: St. Louis, MO (onsite). Experience level: 5+ years.
