DescriptionThe 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 data architecture affects AI outcomes.
- Experience with modern software engineering practices: Git, peer review, automated testing, CI/CD, containers, APIs, logging/monitoring, and secure configuration/secrets management.
- Ability to troubleshoot production of AI behavior, including retrieval failures, hallucinations, tool-calling errors, prompt regressions, latency, cost, and unexpected edge cases.
- Strong client-facing communication skills and the ability 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 well-structured implementation plan.
- Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent practical experience.
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 one or more 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 such as 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 see and 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 by clients and teammates as a trusted engineer who can combine technical depth, sound judgment, and practical delivery.
