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.
In short
Bigcircle staffs LangChain engineers who have shipped it and decide which parts to keep, composition, streaming, and retrievers, and which to replace with a direct provider SDK call. Parsers fail loudly instead of returning a default that looks like an empty result, and streaming is decided on the first pass because adding it later means restructuring the chain. Vetting starts from a chain they built, then breaks a silent parse or a blocked stream and watches them fix it.
Key facts
Definition
A Bigcircle LangChain engineer has shipped it and decides which parts to keep, composition, streaming, and retrievers, and which to replace with a direct provider SDK call.
How Bigcircle does it
- Bigcircle starts vetting from a chain the engineer built.
- The exercise breaks a silent parse or a blocked stream, and Bigcircle watches the engineer make the parser fail loudly or the stream move.
- On the engagement streaming is decided on the first pass, because adding it later means restructuring the chain.
Comparison
A parser that returns a default looking like an empty result hides the failure, while a Bigcircle LangChain engineer makes that parser fail loudly.
Key number
Bigcircle shortlists a LangChain engineer in 2 to 5 days, and the top 3% of applicants remain after five stages.
At a glance
Engineers who know which LangChain abstractions to keep and which to drop.
See which LangChain abstractions a Bigcircle engineer keeps, which they replace with a provider SDK call, and how they make a parser fail loudly.
- Who this is for
- Engineering leaders who need an engineer to work within their existing team.
- Topics
- LangChain
- LCEL
- Runnables
- Retrievers
- Rerankers
- Vector DBs
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?
Common questions
What does a Bigcircle LangChain engineer own?
A Bigcircle LangChain engineer composes chains and runnables that stream instead of blocking on the full response, and knows which abstraction to drop for a direct provider SDK call. They enforce structured output with parsers and schemas that fail loudly, wire retrievers and rerankers so retrieval is measurable, migrate a codebase across LangChain versions without a rewrite, and instrument callbacks into tracing.
How does Bigcircle vet LangChain engineers?
Every Bigcircle LangChain engineer has cleared five stages. Bigcircle starts with a system they already built, then breaks something and watches them fix it. For a LangChain engineer the break is a parser that returns a default instead of raising, or a chain that blocks until the full response arrives. They say how they would know it is working, how they would make it fast without running up the bill, and what they do first when it is late and the AI got it wrong. The top 3% of applicants remain.
How does hiring a Bigcircle LangChain engineer start?
You tell us the AI problem, your stack, and the seniority you need. Bigcircle matches from LangChain engineers who have already cleared vetting, and you see a shortlist in 2 to 5 days. You interview them, or run an optional paid trial on real work, before you commit. They embed in your repositories and your tools and decide which LangChain pieces stay and which become a direct SDK call. You own the code, and if the match is not working Bigcircle replaces them at no extra cost.
What do Bigcircle LangChain engineers fix when a chain breaks in production?
A chain that reads cleanly at length three becomes unreadable at twelve, and callback indirection hides which call produced a bad answer. Bigcircle LangChain engineers keep composition and streaming, and call the provider SDK directly where the abstraction only adds a stack trace. Parsers have to fail loudly, streaming is a first-pass decision, and migrating module by module is the difference between a week and a quarter.
Often hired together.
AI Agent Engineers
Engineers who stop a bad tool call before it runs. Every action is traced so it can be replayed.
RAG & Retrieval Engineers
Engineers who measure retrieval apart from the answer. Chunking is part of the job, because a missing passage cannot be fixed in the prompt.
LLM Engineers
Engineers who fine-tune against your rules and publish the comparison with the base model. Serving changes only when that comparison says the quality held.
Services and guides.
Services
Guides
- Why RAG systems fail in productionMost RAG failures look like model failures until retrieval, context assembly, and generation are measured separately. A plausible answer with a nearby citation can still be the wrong passage.
- How to build production AI agentsAn agent is a loop that observes, acts through tools, and stops. The production question is which decisions the model may make, which code must make, and where a person approves the risk.
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.