Every few weeks I get a message that starts with “we’re looking for a senior engineer who can take our LangGraph pipeline to production and nobody on our team has done this before — what should we look for?” This article is the long-form version of the answer I send back.

The short version

A senior Python agent-platform engineer in 2026 is not a data scientist, not a backend engineer, and not an ML infrastructure engineer. They are a hybrid that is rarer than any of those three individually, and the hiring pipelines most companies use don’t find them.

The skills that actually matter

1. Deep FastAPI, not just FastAPI

Everyone has shipped a CRUD service in FastAPI. Far fewer people have wrestled with: async streaming under load, custom middleware for multi-tenant context, dependency injection for testability, and the interaction between Pydantic 2 validation and long-running background tasks. That’s the real skill.

2. Actual LangGraph production experience

“I’ve built LangGraph demos” is not the same as “I’ve kept a LangGraph pipeline running through a holiday weekend.” Ask candidates to walk through a specific failure mode they debugged — checkpoint corruption, non-deterministic step replay, memory leaks in long sessions. Real answers are detailed. Fake answers are generic.

3. Cost and governance instincts

A senior agent engineer has the reflex to ask “what’s the budget?” before writing the code. They assume cost caps are mandatory. They automatically wire per-tenant tracking. They’ve had the 2am incident and don’t want to have it again.

4. Protocol literacy

MCP, A2A, OpenAPI, WebSocket, SSE — and when to use each. Candidates who only know REST are fine for traditional backend work and not fine for agent platforms.

5. Tracing and observability

OpenTelemetry is not optional. Candidates who have instrumented a production Python service with OTel and know the sharp edges (context propagation in async code, exporter backpressure, cardinality explosion) will save you months. Candidates who’ve heard of OTel but never used it will cost you months.

6. Testing strategies for non-deterministic code

Agents are non-deterministic. Test suites that assume deterministic outputs are useless. Real senior candidates have opinions about: replay harnesses, deterministic seeds, snapshot testing with fuzziness, contract tests for tool calls, and when to mock vs record vs live-call.

7. FastAPI adjacent infrastructure

Redis, Postgres, S3, K8s or a PaaS. They don’t have to be experts — but they have to have strong opinions about how to use each for agent workloads specifically.

Red flags

“I’ll use LangChain for everything.” A senior engineer knows when not to use LangChain. The answer is “often.”
“We’ll add cost tracking later.” No, you won’t. The weekend incident is already on its way.
“Checkpoints are a premature optimization.” Run away.
“Just retry on failure.” Not with agents. Retry with budget checks, idempotency guards, and checkpoint replay.
“I’d write our own MCP server from scratch.” Sometimes the right answer, more often a warning sign that the candidate hasn’t looked at existing tools.
Candidate can’t explain the difference between PydanticAI and LangChain’s Runnable. They’re not current.

Where to find them

Not where you’re looking

The classic “senior backend engineer” pipeline on LinkedIn won’t find these people. Neither will “senior ML engineer” — most ML engineers haven’t shipped FastAPI services. The intersection is small.

Where they actually are

Open-source contributors to FastAPI, Pydantic, LangGraph, LangChain, PydanticAI, or MCP itself. GitHub activity is more predictive than resumes.
Conference talks at PyCon, PyData, and AI Engineer Summit about agent infrastructure specifically.
Niche Discords and Slacks — the PydanticAI community, the LangChain Discord, the FastAPI Discord. Lurkers who help others answer questions are often more senior than they look.
Technical blogs about shipping Python agent services. The good ones are usually on personal domains, not corporate blogs.

Contract vs FTE

Most of the best people in this space are already working somewhere they like. They’re not on the job market — but they do take contracts. A 4–8 week contract is often the fastest way to move forward, and it filters for people who can actually deliver.

What a realistic engagement looks like

If you’re hiring a contractor to get your agent platform to production, here’s the shape we recommend (and the shape we offer at Neul Labs):

Week 1: Audit. Trace logs, checkpoint store state, cost data, failure modes. Deliverable: a remediation plan with prioritized items.

Weeks 2–4: Stabilize. Fix the worst offenders — usually checkpoint durability and cost caps.

Weeks 5–8: Harden. Add observability, policy enforcement, MCP surfaces, and an on-call runbook.

Handoff: Pair with your team on the last two weeks. A handoff that isn’t paired is a handoff that doesn’t stick.

Budget $40k–$120k for a full engagement depending on complexity. Yes, that’s a lot. It’s cheaper than an FTE search that takes nine months and ends with a bad hire.

If you want to talk to us

Neul Labs — the team behind FastAgentic — takes on a small number of these engagements each quarter. We’re the set of people who most of the above paragraphs describe. If your LangGraph pipeline is on fire, or your PydanticAI demo needs to become a product, or your platform team is trying to onboard agents from three different frameworks without doubling the on-call rotation, get in touch.

And if you’re trying to hire someone like us full-time: good luck. Send them this article — they’ll laugh at the red flags list.

Hiring FastAPI and LangGraph experts: what to actually look for