About Oakwood. Oakwood Systems Group is a Microsoft Solutions Partner helping organizations modernize applications, data platforms, cloud environments, and business workflows. Our AI work focuses on moving clients beyond experimentation by designing and delivering secure, production-ready AI applications, agents, copilots, and automation that create measurable business value.

The Role. We are seeking an experienced, hands-on AI Engineer who can turn ambiguous business problems into reliable AI solutions. This role is for an engineer with a strong software, data, or machine-learning foundation who has already shipped production systems and is comfortable owning work from discovery and technical design through implementation, evaluation, deployment, and operational handoff. You will work directly with clients and Oakwood delivery teams to build practical AI capabilities on the Microsoft cloud, with particular emphasis on generative AI, retrieval-augmented generation (RAG), agentic workflows, enterprise integration, and responsible production operations.

What You Will Do:

• Design, build, and deploy production of 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 that produce grounded, source-aware answers.
• Design agentic workflows that use tools, APIs, structured outputs, memory/state, human approval, and multi-step orchestration to automate real business processes safely.
• Integrate AI capabilities with enterprise applications and data sources through REST APIs, Microsoft Graph, Power Platform, databases, SaaS platforms, and custom services.
• Develop maintainable backend services and APIs, primarily in Python, with appropriate testing, error handling, logging, security, and performance considerations.
• Establish evaluation approaches for AI quality, groundedness, reliability, safety, latency, and cost; use those results to improve prompts, retrieval, orchestration, and model selection.
• Implement 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.
• Work directly with client business and technical stakeholders to understand workflows, clarify requirements, prototype solutions, explain tradeoffs, and translate AI possibilities into deployable designs.
• Contribute architecture reviews, code reviews, estimation, technical discovery, and solution design for new client opportunities.
• Create reusable patterns, accelerators, reference implementations, and delivery of assets that improve the speed and consistency of future Oakwood AI engagements.
• Document solutions clearly and support knowledge transfer so client teams can understand, operate, and extend what we deliver.
• Mentor less-experienced engineers and help establish pragmatic engineering standards for production of AI delivery.

Required Qualifications:

• 5+ years of professional engineering 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 of hands-on experience building generative AI / LLM-powered applications or AI-enabled software, with at least one solution taken beyond prototype into a production or production-like environment.
• Strong Python programming 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 rather than subjective demos alone, including quality, reliability, safety, latency, and/or cost.
• Experience with cloud-based applications and AI services; strong Azure experience is preferred, and candidates should be able to work effectively in a Microsoft-centric environment.
• Working knowledge of SQL and enterprise data integration, including the ability to reason about data quality, access, lineage, and how dat…