AI & Machine Learning Staffing — Engineers Who’ve Built in Production
The race to implement generative AI and LLM solutions is creating unprecedented demand for specialized talent. Organizations need 1 million more developers skilled in AI-driven tools by 2026, yet most candidates with AI on their resume lack production-level experience. Your AI initiatives can’t afford hype—you need engineers who’ve shipped real models.
Every engineer applying for AI roles has AI on their resume. The ones who’ve shipped production systems can tell you exactly what went wrong in the first deployment and how they fixed it.
Why AI and Machine Learning Hires Go Wrong
The candidate had a fine-tuned LLM in their portfolio. It was a personal project on a free-tier GPU. Your team needs someone who’s managed inference costs at scale, handled latency constraints in production, and shipped an AI feature to users who didn’t care how clever the model architecture was — they cared that it worked.
The AI talent market is uniquely difficult to screen because the credential gap between genuine production experience and credible-sounding resume language is almost invisible to a recruiter who doesn’t know the domain. Anyone who took a Coursera course can write about transformers. Anyone who built a chatbot prototype can list LangChain. The engineers who’ve actually deployed retrieval-augmented generation at scale, managed model drift in a live product, or fine-tuned a foundation model against proprietary data are a much smaller pool.
A wrong AI hire is expensive in a specific way: AI projects have long ramp times, unclear success metrics, and high expectations from leadership. A mismatched hire doesn’t fail quietly. It burns time, budget, and organizational credibility for a technology that was already facing scrutiny.
The right machine learning recruiter knows the difference before the interview.
How Teak Talent Vets AI and ML Engineers
Every candidate on your shortlist clears three filters before you see their name.
Production Depth
We evaluate what candidates have actually shipped, not what they’ve studied. That means asking about deployment infrastructure, inference latency, model evaluation frameworks, hallucination handling, and how they’ve managed a production model after it went live. Candidates who’ve only worked in notebooks can’t answer these questions in any useful detail. Candidates who’ve shipped can.
Specialization Fit
Generative AI, classical ML, computer vision, NLP, and MLOps are distinct disciplines with different toolchains, different failure modes, and different skill profiles. We identify where your initiative sits and match candidates who’ve worked specifically in that area — not engineers with general AI exposure who’ll need a semester of context before they can contribute.
Organizational Fit
AI engineers sit at the intersection of research, product, and engineering. How they communicate uncertainty to stakeholders, set realistic expectations, and work alongside non-technical teams determines whether an AI initiative succeeds or stalls. We evaluate collaboration patterns and communication style alongside technical depth, so the placement holds inside your actual organizational structure.
The result: AI and ML engineers who’ve already solved problems like yours — and can contribute without a long runway of theoretical catch-up.
AI and Machine Learning Roles We Place
Generative AI and LLM
Machine Learning Engineering
AI Infrastructure and Leadership
If the role involves building, deploying, or scaling AI systems in production, we can source it.
What the Process Looks Like for You
Intake Call
We spend 30 minutes mapping your AI initiative: what you’re building, your current model infrastructure, which frameworks you’re running, where you are in the build vs. buy decision, and what the last hire got wrong. AI roles are context-dependent in ways that change the entire search — and we pull that context out before we source anything.
Candidate Sourcing and Vetting
We work our network and evaluate candidates against your specialization requirements, production experience bar, and team fit. You don’t see anyone who hasn’t cleared all three. That means your engineers’ time goes toward interviews with candidates who’ve actually shipped — not candidates who know the vocabulary but not the work.
Shortlist Delivery
You receive a curated shortlist of qualified candidates. Each profile includes our assessment of fit — production depth, specialization match, organizational alignment — not just a forwarded resume with AI checked off.
Your Interviews
You run the process from here. We stay available to support scheduling, candidate questions, and recalibration if the first shortlist needs adjusting based on what your technical interviews reveal.
Placement and Follow-Through
After an offer is accepted, we stay engaged. If something isn’t working, we work with you to understand what happened and address it. An AI hire that doesn’t hold is a setback for your entire initiative — and that’s not something we consider a finished job.
Related Services
Building a complete technical team? We staff across the full IT stack.
Frequently Asked Questions
A generalist recruiter searches for “AI” or “machine learning” and sends you everyone who listed it on their resume. A machine learning recruiter evaluates whether a candidate has deployed models in production, fine-tuned LLMs against real datasets, or built RAG pipelines that actually perform at scale. At Teak Talent, we assess production experience, not just academic credentials or personal project portfolios — because shipping AI in production is categorically different from studying it.
We evaluate candidates on the specifics of what they’ve shipped: model size, inference latency constraints, fine-tuning approach, vector database selection, evaluation frameworks they’ve built, and how they’ve handled model drift or hallucination in production. Candidates who’ve only studied AI can describe concepts. Candidates who’ve deployed it can describe the problems they ran into and how they solved them. That distinction is the entire vetting process.
Yes. We work across all three engagement types. AI proof-of-concept builds and short-term model projects often call for contract staffing. Long-term ML platform ownership and team buildouts typically warrant permanent placement. The intake call determines which structure fits your initiative and timeline.
Yes. We source against your specific stack — whether that’s PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, OpenAI, Anthropic, or a proprietary fine-tuned model. Framework familiarity matters less than production judgment, but we factor in both. An engineer who’s only worked in one framework and needs six months to ramp on yours is not a fit.
We place engineers across generative AI, large language model development and fine-tuning, ML engineering, MLOps, data science, computer vision, natural language processing, and AI product engineering. We staff for companies across industries including SaaS, fintech, healthcare technology, and enterprise. If it involves building or shipping AI in production, we can source it.
Your AI Initiative Doesn’t Have Time for the Wrong Engineer
Teak Talent places AI and machine learning engineers vetted for production experience, specialization fit, and the organizational context your initiative actually requires. If your current search is stalling, let’s talk.
Get Your ShortlistReady to Accelerate Your AI Initiatives?
Don’t settle for AI talent that’s all theory and no execution. Schedule your discovery call and let’s find the specialists your AI projects demand.