Haystack

Hiring playbook · 2026

How to hire a LLM Engineer

Hire LLM engineers who ship reliable AI features on foundation models. This is the same 5-step playbook our customers run for every hire - start to offer in ~21 days.

14–21d

Time to hire

kickoff to signed offer

2–3

Interview rounds

incl. final

92%

Offer acceptance

vs ~60% industry

~5:1

Shortlist-to-hire

typical ratio

Blueprint

The 5-step process

Each step has a clear owner, a typical duration and a deliverable. Run it like a sprint.

  1. 01

    Define the role and must-have skills

    Day 0 · 1 hr

    Agree the 3–5 non-negotiable skills before sourcing. For a llm engineer, that's typically LLMs, RAG, Evals, Vector databases plus demonstrable experience shipping production systems.

  2. 02

    Decide on level, comp, and working pattern

    Day 0 · 30 min

    Mid-level llm engineers earn around £82k–£115k; senior hires reach £125k–£180k. Confirm hybrid/remote expectations upfront - it's the single biggest deal-breaker on offers.

  3. 03

    Source vetted candidates

    Day 1

    Skip cold sourcing. Haystack matches you with pre-vetted llm engineers actively interviewing, with skills, salary and notice period verified upfront.

  4. 04

    Run a focused 2–3 stage process

    Day 2–10

    Keep it tight: 30-min intro, technical deep-dive, and a final round with team and leadership. Avoid take-homes longer than 2 hours - top candidates won't engage.

  5. 05

    Reference, offer, and onboard

    Day 10–14

    Move fast on offer once a decision is made. Senior llm engineers often have multiple processes running; a 24–48 hour offer window is the new normal.

£82k–£115k

Mid-level base

Anchor your comp band around the mid-level number. A senior llm engineer reaches £125k–£180k; juniors start near £55k–£72k. Add ~10–15% for London and Berlin, and 25–40% for SF and NYC, where total comp dominates base.

Must-have vs nice-to-have skills

4 core · 4 nice to have

Core stack

LLMsRAGEvalsVector databases

Nice to have

LangChainLlamaIndexPrompt engineeringPython

Watch-outs

Common mistakes that kill llm engineer hires

Vague job description

Skills like "LLMs" need years of experience and context. Specify it.

Too many interview rounds

Top candidates drop after the 3rd. Cap at 3, including final.

Lowballing on offer

Internal salaries go stale fast. Benchmark every 6 months - not yearly.

Skipping references

Live-coding catches what dialogue won't. Always do at least one paired session.

Slow offer turnaround

48 hours after final round is the upper bound. Faster wins the candidate.

No defined scorecard

Hiring 'gut feel' alone leads to inconsistent decisions across panels.

What a great llm engineer owns

Use this as your interview scorecard. Score each candidate 1–5 per item; calibrate as a panel.

  • Ship LLM-powered product features end-to-end
  • Own evals, prompt iteration and model selection
  • Design retrieval, tool-use and agent workflows
  • Balance latency, cost and quality in production

Deep dive

The llm engineer hiring playbook

LLM Engineer specialist or generalist - which should you hire?

The honest answer depends on the half-life of your llm engineer surface area. If you expect to keep investing in LLMs and RAG work over the next 18-24 months, a specialist llm engineer will out-deliver a generalist on day-30 throughput and stakeholder confidence.

If your team is under ten people, or llm engineer responsibilities are spread across two or three roles already, hire a strong generalist who has shipped this work in anger at least twice. The cross-disciplinary pattern recognition will pay for itself the first time priorities collide.

On Haystack we surface both - filtered by whether the candidate self-identifies as a llm engineer specialist and verified against their last two roles. Expect to pay around £82k–£115k for a mid-level UK hire, scaling toward £125k–£180k for senior.

What strong llm engineers actually bring

A great llm engineer is not the one with the longest CV - it is the one who has owned a hard LLMs call and changed how they work because of how it landed. Across the engineering hires we have placed in 2025-2026, the same patterns keep showing up.

  • Active mentorship of at least one other llm engineer or adjacent role - usually a junior - within the first quarter.
  • Versioned, observable llm engineer work - measurable outputs, structured logs of decisions, and a clear rollback path on every change.
  • Documented trade-off notes on the calls they made, including the option they rejected and why.
  • An opinion on what NOT to do with LLMs, backed by an example where adding it would have hurt the team.

Red flags when interviewing llm engineers

Every discipline has its own pattern of plausible-sounding answers that fall apart in production. For llm engineers, these are the patterns that most often correlate with a six-month regret hire on the employer side.

  • Cannot name a single llm engineer project where they removed scope rather than added it.
  • Defines "senior llm engineer" purely by years of experience, not by the scope of decisions they own.
  • Lists LLMs on the CV but cannot describe a single trade-off they hit in production - all framework, no friction.
  • Treats the llm engineer role as a job title rather than a problem to solve - no opinion on what they would change about how the discipline is typically practised.

What to expect in the first 30 days from a Haystack llm engineer hire

By week one, the new llm engineer should have shipped a small, low-risk artefact to production or a stakeholder - a docs fix, a small process change, a first review on someone else's work. The goal is to validate the loop, not to ship anything heroic.

By week two, the llm engineer is shadowing the active workstreams, attending standups in observe-mode, and asking pointed questions about why specific decisions were made. If they are not asking those questions, the hire is going to plateau.

By day 30, they own one cleanly-scoped slice of the llm engineer surface area, have published a public ramp-up doc, and are the named point of contact for stakeholders inside that slice. Every Haystack employer gets a structured onboarding template, so you are not reinventing the playbook each hire.

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