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Staff LLM Engineer — RAG & agents
DeepMind
Este anuncio está pensado para talento nativo en IA : habilidades y herramientas claras para saber si encajas antes de aplicar — y para reducir descartes por desajuste.
4 habilidades 2 herramientas
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Modalidad Hybrid
Ubicación 5 New Street Square, London, England EC4A 3TW, United Kingdom · Hybrid
Compensación £140k–£180k + bonus + equity Nivel de experiencia Beneficios Private medical, pension, learning budget, hybrid 3/2. Cómo aplicar Link a production RAG or agent system you led (redacted OK).
Publicada 25 may 2026
Actualizada 8 jul 2026 DeepMind is hiring a Staff Staff LLM Engineer — RAG & agents 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.
DeepMind builds frontier AI for science and products used by Google and partners worldwide. Our London teams work on large-scale training, alignment, and safe deployment of language and multimodal systems.
We are hiring a Staff LLM Engineer (hybrid, London) to lead retrieval-augmented and agentic systems for research tooling and internal products—with rigorous evals and reproducible releases.
What you will do
Ship production LLM and retrieval systems with clear evaluation, monitoring, and rollback paths—not demo-only notebooks. Partner with product, design, and domain experts on requirements, trade-offs, and safe rollout. Improve data quality, labeling, and offline metrics that correlate with business KPIs. Document architectures and runbooks for on-call and customer-facing teams. Mentor engineers on testing, observability, and responsible AI practices. What we look for
Strong Python and modern ML/LLM stack experience with multiple shipped features. Comfort with SQL, experimentation, and communicating results to non-technical stakeholders. Pragmatic ownership from prototype to production—including cost and latency budgets. Eligible to work in the listed country; professional English (French a plus for Paris roles). 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
Calling frontier APIs with retries, cost controls, prompt versioning, and safety filters.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
Docker Tool
Containerized services and reproducible dev environments for ML and LLM workloads.
Kubernetes Tool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
✓ You can show evidence in your application: Link a production RAG or agent system you led (redacted OK). ✓ Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, PyTorch, and Docker. ✓ 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: £140k–£180k + bonus + equity, benefits in At a glance) and your expectations align. Lo que pide la empresa
Link a production RAG or agent system you led (redacted OK).
Inicia sesión con cuenta candidato para aplicar
Regístrate, añade titular y bio; luego podrás adjuntar enlace o PDF de CV en Mi cuenta antes de aplicar. Así las candidaturas quedan ligadas a tu perfil.
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Título
Staff LLM Engineer — RAG & agents
Aplicar Pensado para equipos que contratan personas que trabajan con IA, no solo alrededor.
Los candidatos ven habilidades y herramientas al inicio; recibes candidaturas estructuradas en un solo panel.
Llega a profesionales de IA que buscan roles con expectativas basadas en pruebas.
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