Hire senior LangChain engineers in your time zone.

A senior LangChain engineer owns the reliability of a system that is inherently less predictable than typical software: the same input can produce different output, and a single weak link in a chain (a bad chunk, a flaky tool call, a hallucinated step) can break the whole flow.

Signals of real seniority
Look for engineers who talk about retrieval quality and chunking strategy specifically, not just "we used LangChain and a vector database." They should be able to describe how they tested whether retrieved context was actually relevant, and how they handled it when it wasn't.

• A concrete chunking and retrieval strategy tuned for the type of documents involved
• Experience debugging agent loops that call the wrong tool or get stuck retrying
• A repeatable evaluation process run against a fixed set of test cases before shipping changes

These are the specifics BetterEngineer's vetting process checks for before a LangChain candidate reaches your interview stage.

A RAG pipeline or agent that works in a demo and one that holds up in production are different systems. Production versions need to handle retrieval that returns irrelevant context, tool calls that time out, and users who ask questions the system was never designed for.

What production discipline looks like
Ask how a candidate monitors an LLM application after launch. Cost per query, latency per chain, and output quality all need tracking, since a small prompt change or a model provider update can silently degrade results.

• Structured evaluation sets used before and after any prompt, retrieval, or model change ships
• Fallback logic for when a tool call fails or an LLM response doesn't parse as expected
• Cost and latency budgets set per workflow, since chained LLM calls compound both quickly

BetterEngineer looks specifically for this production discipline, not just familiarity with the LangChain API, when vetting candidates for LLM application roles.

LangChain moves quickly as a library, and its surface area is broad enough that fluency with the API tells you little on its own. The differentiator is whether a candidate has actually shipped a RAG pipeline or agent that real users depend on, and can describe what went wrong along the way.

Questions worth asking directly
Have the candidate describe a specific chain or agent they built: what data it retrieved from, how they tested whether retrieval was actually relevant, and what they changed after seeing real user queries. Answers that stay abstract, without a specific chunking strategy or evaluation approach, are a warning sign.

• Walk me through a RAG pipeline you built. What was your chunking and retrieval strategy, and how did you know it worked?
• Describe an agent that failed in production. What broke, and how did you fix it?
• How do you evaluate whether a prompt or retrieval change is actually an improvement before shipping it?

BetterEngineer runs candidates through this same line of questioning before you ever see a profile, so the depth is already confirmed.

Quick evaluation checklist:

• Ask for a RAG or agent system they shipped, including what happened when retrieval returned the wrong context
• Check their evaluation process for LLM output quality, not just anecdotal "it looks right" testing
• Probe their approach to cost and latency when a workflow chains multiple LLM calls together
• Ask how they handle failure gracefully when a tool call, API call, or retrieval step breaks
• Confirm they can explain a chunking and retrieval strategy, not just which vector database they used