AI role · Open offer
Staff LLM Engineer — Agents & enterprise RAG
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Thames Inference Labs
- Full-time
- Staff
- Applied LLM
- Hybrid
- London, England, United Kingdom · Hybrid
- London, United Kingdom
- £120k–£155k + bonus + equity
This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- London, England, United Kingdom · Hybrid
- Compensation
- £120k–£155k + bonus + equity
- Experience level
- Staff
- Team
- Applied LLM
- Benefits & perks
About this role
What the team is looking for and how this position fits.
Thames Inference Labs is hiring a Staff Staff LLM Engineer — Agents & enterprise RAG on the Applied LLM team (Hybrid · London, United Kingdom). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
Thames Inference Labs ships assistant and RAG platforms for regulated customers in banking, insurance, and critical infrastructure. We blend pragmatic delivery with strong evaluation and rollback discipline so models never become a black-box liability.
We are hiring a Staff LLM Engineer based in London (hybrid). You will anchor technical decisions for retrieval, tool-calling, and evaluation—partnering daily with applied scientists, security, compliance, and forward-deployed teams.
What you will do
- Architect multi-tenant RAG pipelines: ingestion, ACL-aware retrieval, citations, summarisation policies, and human review hooks where required.
- Implement agent workflows with deterministic fallbacks—tracing, cost controls, idempotent tools, and clear failure modes.
- Own offline and online evals (golden sets, regressions, calibrated LLM-as-judge) and gates before production prompt or model changes.
- Drive latency and reliability SLOs with infra partners; tune batching, caching, and serving paths for flagship workloads.
- Produce crisp RFCs and runbooks so customer-facing teams can operate flows safely after launch.
What we look for
- 7+ years backend or ML systems experience with multiple production LLM or retrieval launches.
- Expert Python and modern orchestration (LangChain/LangGraph or equivalent); deep familiarity with vector DBs and OpenAI-compatible APIs.
- Comfort owning ambiguous problem statements and communicating trade-offs to non-engineering stakeholders.
- Demonstrable mentoring and code-review habits; care for tests and observable systems.
- Eligible to work in the UK; professional English.
Nice to have
- Kubernetes, Terraform, and observability stacks (OpenTelemetry, Grafana) in regulated environments.
- Experience with EU AI Act, DPIA artefacts, or security-driven release processes.
- Conference talks, OSS, or blog posts on agents or retrieval.
Skills we're hiring for
What each capability means for this role—not a keyword checklist.
- Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
- LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
- OpenAI API
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- AWSTool
Regular use of AWS in the LLM & generative AI stack—configuration, debugging, and team conventions.
- DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
You're a strong fit if…
Tailored to this listing—use it as a self-check before you apply.
- ✓You can show evidence in your application: Share one production agent or RAG design doc (redacted OK).
- ✓Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, Vector DBs, and AWS.
- ✓You have led LLM & generative AI initiatives across teams—setting technical direction, review standards, and rollout plans others could follow.
- ✓You are comfortable with the hybrid rhythm (Hybrid · London, United Kingdom)—onsite collaboration when it matters, deep work when remote.
- ✓You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
- ✓You read the full listing (comp: £120k–£155k + bonus + equity, benefits in At a glance) and your expectations align.
Apply
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What the employer asked for
Share one production agent or RAG design doc (redacted OK).
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