AI Engineer

Oakwood Systems Group Inc. is seeking an experienced, hands-on AI Engineer to design, build, and deploy secure, production-ready AI applications, copilots, and agents on the Microsoft cloud. The work centers on generative AI and practical enterprise delivery, including retrieval-augmented generation (RAG), agentic workflows, integration with business systems, and measurable evaluation in production.

What you will do

• 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 that produce grounded, source-aware answers.
• Design agentic workflows that use tools and 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, with testing, error handling, logging, security, and performance in mind.
• Create 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.
• Use enterprise security and governance practices covering identity, role-based access, data boundaries, auditability, responsible AI controls, and human-in-the-loop patterns when appropriate.
• Collaborate with client business and technical stakeholders to understand workflows, clarify requirements, prototype solutions, explain tradeoffs, and translate AI opportunities into deployable designs.
• Support new client opportunities with 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 future AI engagements.
• Document solutions clearly and support knowledge transfer so client teams can operate and extend what is delivered.
• Mentor less-experienced engineers and help establish pragmatic engineering standards for production AI delivery.

Required qualifications

• 5+ years of 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 production or production-like environments.
• Strong Python skills, including 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, including quality, reliability, safety, latency, and/or cost.
• Experience with cloud-based applications and AI services, with strong Azure experience preferred and the 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 affects AI outcomes.
• Experience with modern software engineering practices including 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 unexpected edge cases.
• Strong client-facing communication skills to explain technical concepts, architecture decisions, risks, and tradeoffs to both 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.

Technologies

• Microsoft Azure, Python, REST APIs, Microsoft Graph, Power Platform
• SQL, Git, CI/CD, containers
• Microsoft Foundry, Azure AI Foundry, Azure OpenAI, Azure AI Search, 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
• Microsoft Fabric, Databricks
• GitHub Copilot, Claude Code, Codex, Cursor

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 including evaluation pipelines, model or prompt versioning, tracing, monitoring, feedback loops, and production incident response.
• Experience with Microsoft Fabric, Databricks, or other modern data platforms used to prepare governed enterprise data for AI.
• Experience with classical machine learning, NLP, deep learning, fine-tuning, multimodal models, or model serving where the use case requires more than foundation-model APIs.
• Experience using AI-assisted development tools such as GitHub Copilot, Claude Code, Codex, or Cursor while applying disciplined engineering review for correctness, security, and maintainability.
• Prior consulting, professional services, systems integration, or Microsoft partner experience.
• Relevant Microsoft or cloud certifications, such as Azure AI Engineer, Azure Developer, Azure Solutions Architect, or equivalent.

What success looks like

• AI solutions move from discovery and prototype to secure, supportable production deployments with clear acceptance criteria.
• Clients can articulate the business value of the systems delivered, not just the technical novelty.
• AI quality is measured and improved through repeatable evaluation, observability, and feedback rather than one-time demonstrations.
• Solutions integrate cleanly with client applications, data, identity, and operating processes and can be maintained after handoff.
• Reusable engineering patterns and delivery assets reduce effort and risk across future engagements.
• You are viewed as a trusted engineer who combines technical depth, sound judgment, and practical delivery.

Location: Kansas City, MO (onsite)

Experience: 5+ years