Rol IA · Oferta abierta

Senior ML Engineer — LLM observability

Índice de mercado

Datadog

Tiempo completo
Senior
AI monitoring
Hybrid
13 Clerkenwell Close, London, England EC1R 0AA, United Kingdom · Hybrid
London, United Kingdom
£105k–£140k + equity

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Empleo: Tiempo completo
Modalidad: Hybrid
Ubicación: 13 Clerkenwell Close, London, England EC1R 0AA, United Kingdom · Hybrid
Compensación: £105k–£140k + equity
Nivel de experiencia: Senior
Equipo: AI monitoring
Beneficios

Sobre el rol

Qué busca el equipo y cómo encaja el puesto.

Datadog is hiring a Senior Senior ML Engineer — LLM observability on the AI monitoring 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.

Datadog helps thousands of teams monitor cloud and AI workloads. London engineers extend observability to LLM traces, eval scores, and cost attribution.

Senior ML Engineer — LLM observability: build product surfaces for prompt versioning, drift detection, and red-team signal aggregation.

Ideal if you have shipped agent or RAG systems and wished you had better production visibility on day one.

What you will do

Design metrics and traces for LLM chains, tool calls, and retrieval hops.
Prototype eval integrations (LLM-as-judge, golden sets) inside Datadog workflows.
Work with enterprise customers on secure VPC deployments and ACL-aware logging.
Contribute to open standards for AI observability where possible.

Stack & environment

Python, OpenAI-compatible APIs, LangChain, SQL analytics.
Kubernetes, Docker, high-cardinality time-series backends.
Hybrid London Clerkenwell hub.

What we look for

Production LLM or MLOps experience with measurable reliability wins.
Strong debugging skills across distributed systems.
Right to work in the UK.

Habilidades que buscamos

Qué implica cada capacidad en este rol — no solo una lista de palabras clave.

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

Herramientas y stack

Plataformas y productos mencionados para este rol (APIs, IDEs, BBDD vectoriales, CRM, diseño, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

Encajas muy bien si…

Adaptado a esta oferta — úsalo para comprobar tu encaje antes de aplicar.

You can show evidence in your application: Describe eval dashboards or tracing for LLM pipelines you built.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, SQL, and Kubernetes.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
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.

Aplicar

Solo cuentas candidato: añade tu titular y bio en , y opcionalmente un enlace o PDF de CV para que los reclutadores vean tu CV completo. Luego escribe un breve pitch — las candidaturas usan el correo de tu cuenta.

Lo que pide la empresa

Describe eval dashboards or tracing for LLM pipelines you built.

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.

Iniciar sesión Crear cuenta candidato

Sugerencias

Talento IA que podría interesarte

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