AI & ML Recruiting
The AI talent market moves fast and misfires are expensive. We combine technical screening, active network sourcing, and engineering judgment to find engineers who can actually build.
The Problem
Most companies underestimate how different hiring AI talent is from hiring general software engineers. Three problems keep coming up.
Everyone claims AI expertise. PyTorch on a resume, a Coursera cert, and one toy project look the same as four years of production ML. Without technical depth in the screening stage, you can't tell the difference.
Requirements shift every quarter. The role you wrote a JD for in January might need completely different skills by Q3. A recruiter who understands the space adapts the search — one who doesn't keeps sourcing for the wrong thing.
Real ML and AI engineers are rare, often passive, and not scanning job boards. Finding them requires active outreach into a technical network — not posting on LinkedIn and hoping.
AI Roles We Fill
From foundational model work to production deployment, applied AI to governance — we recruit across the full AI/ML landscape.
Model training, evaluation pipelines, MLflow, feature stores
OpenAI API, Anthropic, Mistral, fine-tuning, RLHF workflows
Retrieval-augmented generation, vector DBs, chunking strategies, hybrid search
Chain-of-thought, structured outputs, system prompt architecture, eval design
Model serving, CI/CD for ML, Kubeflow, SageMaker, model monitoring
AI product strategy, roadmapping with ML constraints, eval frameworks for PMs
Transformers, BERT, YOLO, object detection, OCR, document AI
Responsible AI, model bias auditing, EU AI Act compliance, NIST AI RMF
Enterprise AI integration, reference architecture, vendor evaluation
Our Screening Difference
Most AI recruiting stops at "does this person know PyTorch?" We go deeper. We assess whether a candidate has actually shipped AI systems into production — and whether they understand the tradeoffs that come with that.
Our technical screening covers RAG pipeline design, vector database selection and tuning, LangChain and OpenAI API architecture, production ML systems, and the judgment to know when a simple model beats a complex one. Paper credentials tell us nothing about that.
Model deployment & infra
LLM & RAG architecture
ML fundamentals
Who We Work With
Why companies choose Engineers in AI
Because the AI hiring mistakes we prevent — hiring a researcher when you need a builder, or a prompt wrapper when you need an architect — cost more than the fee we charge.
Ready to hire?
Tell us what you're building and what you need. We'll tell you honestly whether we can help — and what the search would look like.