AI Engineer

Oakwood Systems Group Inc.; Kansas City (MO); On-site; USD 120,000 - 160,000; Full time

Job summary

Oakwood Systems Group Inc. is seeking an experienced AI Engineer to design, build, and deploy secure, production-ready AI applications, copilots, and agents on Microsoft Azure.

You will drive RAG implementations, integration with business systems, and measurable evaluation in production. The role emphasizes hands-on Python development, Azure expertise, and scalable backend services, with a focus on safety, observability, and governance.

Qualifications

• 5+ years of professional engineering across software, ML, data or related disciplines with ownership of production systems.
• 2+ years building gen AI/LLM-powered apps, production-ready.
• Strong Python skills for production services, APIs, data pipelines.
• Hands-on with prompt design, RAG, embeddings, vector search, structured outputs, tool calls, agent orchestration.
• Ability to evaluate AI systems with measurable criteria (quality, reliability, safety, latency, cost).
• Cloud-based apps and AI services; strong Azure experience in a Microsoft-centric environment.
• Working knowledge of SQL and data integration; data quality, access, lineage considerations.
• Experience with Git, automated testing, CI/CD, containers, logging/monitoring, secret management.
• Troubleshoot production AI behavior: retrieval failures, hallucinations, latency, costs, edge cases.
• Strong communication to explain concepts and tradeoffs to engineers and business stakeholders.
• Ability to work independently in a consulting environment; manage priorities from incomplete requirements.
• Bachelor’s degree in CS/Engineering/Data Science or related field, or equivalent practical experience.

Responsibilities

• Design, build, and deploy production AI apps, copilots, and agents on Azure.
• Develop RAG and enterprise knowledge solutions with embeddings and vector search.
• Create agentic workflows with tools, memory, approval steps, and orchestration.
• Integrate AI with enterprise data via REST APIs, Graph, Power Platform, and databases.
• Develop backend services and APIs in Python with testing and security in mind.
• Create evaluation methods for AI quality, groundedness, reliability, safety, latency, cost.
• Apply production engineering practices: CI/CD, containers, observability, secrets mgmt.
• Follow security/governance practices for identity, data boundaries, and responsible AI.
• Collaborate with clients to understand workflows and translate AI opportunities into deployable designs.
• Support opportunities with architecture reviews, discovery, and solution design.
• Create reusable patterns, accelerators, and reference implementations.

Skills

5+ years experience

2+ years GenAI

Python proficiency

LLM patterns

Azure cloud

SQL knowledge

Git/CI/CD

API design

production AI

client-facing

independent consulting

Bachelor's degree

Education

Bachelor's degree in CS/Eng/Data Science

Tools

Microsoft Azure

Python

REST APIs

Microsoft Graph

Power Platform

Job description

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