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Hire generative-AI engineers who ship to production.

The all-rounders of modern AI. They take a generative-AI idea from prototype to live product, wiring models, retrieval, tools, and a real interface into something your customers use every day. Hire one to own a feature solo, or to anchor a pod of the deeper specialists below.

Hire generative ai engineerShortlist in 2–5 days

At a glance

All-rounders who ship a whole AI feature end to end, or anchor a pod of the specialists below.

Review the work Generative AI Engineers can own, the skills required, and the systems they can help you build.

Who this is for
Engineering leaders who need an engineer to work within their existing team.
Topics
  • Claude
  • GPT
  • Llama
  • Fine-tuning
  • Vector DBs
  • Hybrid search

What they own.

  • Design and ship LLM-powered features end to end, from prompt to product
  • Build RAG pipelines grounded in your own data, with a source behind every answer
  • Wire models into your systems through MCP tools and APIs
  • Stand up evals so quality is measured, not guessed
  • Tune for latency and cost so it holds up under real load
  • Work fluently across Claude, GPT, and open models

What you can ship with them.

Customer-facing assistants and copilots
RAG search over internal knowledge
Document and workflow automation
Internal AI tools that cut manual work
Agentic features that take real actions

Tools they reach for in production.

Models
ClaudeGPTLlamaFine-tuning
Retrieval
Vector DBsHybrid searchReranking
Build
PythonTypeScriptMCPLangGraphLangChain
Run
EvalsObservabilityCloud

Seniority: From strong mid-level builders to staff engineers who have led AI at scale.

Five stages.
The top 3% remain.

Every stage asks the same question: can they keep AI running once real customers are using it? Getting something started is the easy part, and it is not what we screen for.

400 applicants

3%of applicants reach
your shortlist

What removes them

  1. We start with something they built

    100% → 20%

    One real system, pressed hard. How much traffic did it take? What broke first? Who got the call when it did?

  2. We break something and watch them fix it

    20% → 9%

    A working system with a bug hidden inside it. Anyone can build a demo in a weekend. Fixing code you have never seen is the actual job.

  3. How will you know it is working?

    9% → 5%

    Before they write anything, they have to tell us how they would test it, and what they would do when it gets an answer wrong.

  4. Make it fast without running up the bill

    5% → 4%

    We give them a speed target and a budget, then ask them to explain the tradeoffs they made to hit both.

  5. It is late and the AI got it wrong

    4% → 3%

    What do you do first? How do you find out what happened, undo it, and explain it to the customer in plain words?

Often hired together.

LLM Engineers

Model specialists who fine-tune, quantise, and serve the model itself.

View role →

AI Agent Engineers

Builders of agents that plan and take real actions in your systems, safely.

View role →

RAG & Retrieval Engineers

Retrieval specialists who ground every answer in your data.

View role →

Find the people to accelerate your roadmap.

You don’t need more resumes. You need proven AI engineers embedded in your workflow and ready to build from day one. Tell us what’s missing and we’ll line up a shortlist.

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