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About This Role
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 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.
Why Oakwood
Oakwood combines deep Microsoft expertise with hands-on application, data, cloud, and AI engineering. We hold Microsoft Solutions Partner designations across key solution areas and an Advanced Specialization for AI Applications on Microsoft Azure. AI Engineers at Oakwood have the opportunity to work directly with clients, build real production solutions, shape reusable delivery patterns, and help organizations turn rapidly evolving AI capabilities into durable business outcomes.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Oakwood Systems Group, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Oakwood Systems Group AI Hiring
Oakwood Systems Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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