Hire engineers who build agents that do real work.
Engineers who build agentic systems that plan, act, and check themselves, connected to your real tools through MCP, with the guardrails that keep autonomy from becoming a liability.
At a glance
Builders of agents that plan and take real actions in your systems, safely.
Review the work AI Agent 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
- LangGraph
- MCP
- Tool calling
- CrewAI
- Guardrails
- Self-checks
What they own.
- Design multi-step agents that plan, act, and review their own work
- Connect agents to your systems safely through MCP tools
- Build the guardrails that stop bad actions before they happen
- Handle long-running and recurring workflows end to end
- Add human-in-the-loop checkpoints where the stakes are high
- Instrument agents so every action is traceable and replayable
What you can ship with them.
Tools they reach for in production.
- Orchestration
- LangGraphMCPTool callingCrewAI
- Control
- GuardrailsSelf-checksHITL
- Build
- PythonTypeScriptClaude / GPT
- Run
- TracesEvalsObservability
Seniority: Engineers who have shipped autonomous workflows, not demo-stage agents.
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
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?
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.
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.
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.
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.
Generative AI Engineers
All-rounders who ship a whole AI feature end to end, or anchor a pod of the specialists below.
LLM Engineers
Model specialists who fine-tune, quantise, and serve the model itself.
MLOps & AI Infra Engineers
The infra people who keep AI fast, efficient, and observable in production.
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.