Hire engineers who have taken LangChain to production.
LangChain is fast to prototype in and easy to over-adopt. The decision that matters on a production codebase is which parts to keep (composition, streaming, retrievers) and which to drop in favour of a direct provider SDK call. Our engineers have shipped LangChain systems in production, and harder stacks than it.
What they own.
- Compose chains and runnables that stream properly instead of blocking on the full response
- Know which abstraction to drop under, and call the provider SDK directly where it is clearer
- Enforce structured output with parsers and schemas that fail loudly rather than silently
- Wire retrievers and rerankers so retrieval is measurable instead of assumed
- Migrate a codebase across LangChain versions without a rewrite
- Instrument callbacks into real tracing rather than console logs
What actually breaks, and what they do about it.
The abstraction stops paying for itself
A chain that reads cleanly at three steps becomes unreadable at twelve, and the callback indirection makes it hard to see which call actually produced a bad answer. The fix is knowing the seam: keep composition and streaming, drop to the provider SDK where the abstraction is only costing you a stack trace.
Output parsing fails silently
A parser that cannot read the model's response often returns a default rather than raising, so a broken extraction reaches your database looking like a valid empty result. Schemas have to fail loudly, and a failed parse needs a repair path rather than a retry of the same prompt.
Streaming is bolted on last
Chains assembled without streaming in mind block until the full response arrives, which is fine in a notebook and unacceptable in a product. Retrofitting it late usually means restructuring the chain, so it is a first-pass decision.
Version drift across a codebase
Package splits and import moves leave a large codebase pinned to an old version because upgrading is a rewrite nobody scheduled. Migrating incrementally, module by module, is the difference between a week and a quarter.
What you can ship with them.
Tools they reach for in production.
- Framework
- LangChainLCELRunnables
- Retrieval
- RetrieversRerankersVector DBs
- Output
- Structured outputParsersSchemas
- Run
- TracingEvalsStreaming
Seniority: Engineers who have already worked on this stack, and on harder ones than it.
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.
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.