AI · LLM · RAG · MLOps · Headhunting

AI & LLM
Recruitment & Headhunting
Global

Specialist headhunting for companies building with large language models — engineers who can ship, product leads who understand the stack, and executives who have done it before.

Discuss a search

Why AI hiring is different

The AI talent market is thin at the top and flooded at the bottom. Everyone can claim "AI experience" after a weekend with the OpenAI API. The people who can actually architect a production RAG pipeline, fine-tune a model without catastrophic forgetting, or lead an AI product team through a real deployment — are employed, not looking, and receive outreach every week.

Finding them requires more than a LinkedIn search. It requires knowing the community, understanding the work, and reaching out in a way that is credible enough to get a response.

What I recruit for

I cover the full spectrum of AI/LLM roles — from hands-on engineers to product leadership:

Roles covered

Engineering

LLM Application Engineers
RAG & Retrieval Architects
ML / MLOps Engineers
AI Infrastructure Engineers
Fine-tuning Specialists
Prompt Engineers
AI Safety / Alignment Engineers
Data Scientists (LLM-focused)

Product & Design

AI Product Managers
LLM Product Leads
AI UX / Conversational Designers
AI Trainers & RLHF Specialists
Technical Product Managers (AI)
AI Evaluation Leads

Applied & Research

Applied AI / Applied ML Scientists
NLP Engineers
Computer Vision Engineers
AI Research Engineers
Knowledge Graph Engineers
AI Integration Engineers

Leadership

Head of AI / VP AI
Chief AI Officer
CTO (AI-native company)
VP Engineering (AI)
Director of ML
Head of Data & AI

The AI talent market in 2025–2026

The LLM talent market is unlike anything in previous tech cycles. The number of companies building AI products has exploded — but the pool of engineers who have shipped production LLM systems is still small. Demand is global; supply is concentrated in a handful of cities and remote-distributed networks.

Key dynamics:

Experience gap at the top

There are many ML engineers. There are far fewer who have built and maintained a production RAG pipeline at scale, handled context window management in a live product, or led an evaluation framework for a mission-critical LLM. These people are in very high demand.

Remote-first by default

The strongest AI engineers work on their terms. Most top candidates expect remote-first or async-first environments. Companies that require full-time office attendance in a tier-2 city are at a structural disadvantage in this market.

Compensation expectations

Senior LLM engineers: $150k–$280k+ (US market), £90k–£160k (UK). AI PMs at well-funded startups: $130k–$200k. Equity expectations are high. Companies entering this space from traditional tech backgrounds are often surprised by the benchmarks.

Speed matters

Top AI candidates are in multiple active processes simultaneously. A slow interview loop loses them. I work with clients who can move decisively when the right person surfaces.

LLMApplication engineers, RAG architects and fine-tuning specialists — the hardest roles to fill in tech right now

RAGRetrieval-Augmented Generation expertise — production experience rare, demand extreme

MLOpsInference optimisation, model serving, monitoring — the operational layer most AI teams understaff

GlobalUS · UK · EU · UAE · Asia — I run searches across time zones and markets

How the search works

Discovery call

We define the role precisely — stack, model architecture, infra context, team structure and timeline. For AI roles, technical context matters: is this a fine-tuning role, a RAG build, a model serving challenge? The brief shapes the entire search.

Active headhunting

I do not post job ads and wait. I map the global AI talent pool for the specific profile, identify candidates by actual work — repos, papers, projects, community contributions — and reach out directly. This surfaces people who are not on job boards.

Screening and shortlist

Each candidate is assessed in depth — technical background, production AI experience, LLM stack familiarity, motivation and fit. You receive 3 to 5 profiles with full assessment notes, not 20 CVs to sift through.

Interviews and close

I coordinate interviews, provide candidate feedback loops, support offer structuring and manage the close. In a competitive market, speed and communication quality at offer stage are what separate wins from losses.

Frequently asked questions

Common questions from companies hiring AI and LLM talent globally.

How do you assess LLM engineering candidates?

I screen for production experience — not tutorials. I ask about the actual systems they have built: how they handled context management, what retrieval strategy they used, how they monitored model performance in production, how they dealt with hallucinations in a live product. Candidates who have only done side projects or coursework respond very differently to these questions than engineers who have shipped real systems.

Can you find candidates for fully remote roles?

Yes — and this is the default in the AI market. The strongest AI engineers are distributed globally. I run searches with no geographic constraint for companies that hire remotely, and can also focus on specific time zones or regions if needed for collaboration reasons.

What is the typical timeline for an AI engineering search?

Most searches take 3 to 5 weeks from brief to shortlist. Senior and specialised roles — RAG architects, fine-tuning specialists, AI safety engineers — can take 4 to 6 weeks given the smaller candidate pool. Timeline is significantly influenced by how quickly the client can run interviews once shortlisted candidates are presented.

Do you recruit AI product managers as well as engineers?

Yes. AI PMs are increasingly in demand — and increasingly rare. I look for product managers who have shipped LLM-powered products, understand evaluation frameworks and can work credibly with research and engineering teams. This is a different profile from a standard technical PM.

Do you work on a retained or contingency basis?

Both, depending on the role and company stage. For senior and specialised AI roles — Head of AI, LLM architects, AI executive search — a retained structure ensures full commitment and priority allocation. For mid-level engineering roles, contingency is available. We discuss the right model at the initial call.

Can you recruit for AI roles at non-AI companies?

Yes. Many of my clients are traditional companies — fintech, enterprise software, e-commerce — that are building AI capabilities for the first time. The challenge is often the same: the best AI engineers prefer working on AI-first products. I help these companies position the role honestly and find candidates who are motivated by the greenfield opportunity.

Hiring for an AI or LLM
role?

Send me a brief outline of the role. I will respond within 24 hours
with a market assessment and realistic timeline.

Get in touch