Senior AI Platform Engineer – Production-Grade & Regulated

Capgemini; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Disability Confident Employer

Learning opportunities

Wellbeing programs

Job summary

Capgemini is seeking a production-focused Platform Engineer to build and operate AI infrastructure for regulated industries. You will own platform components like the model gateway, agent runtime, evaluation harness, and guardrail engine, collaborating with product teams to scale self-service services across multiple lines.

You will work on reinforcement learning readiness, model serving, and multi-tenant SaaS capabilities, contributing to a secure, observable, and cost-efficient cloud platform.

Qualifications

• Strong production engineering: Python plus one systems language (Go or Rust welcome), Kubernetes, infrastructure-as-code, and one major cloud.
• Hands-on experience with LLM infrastructure: a model gateway pattern (such as LiteLLM or in-house), model serving (such as vLLM or managed endpoints), and vector or retrieval systems.
• Agent-systems experience: you have built with an agent orchestration framework (such as LangGraph or first-party agent SDKs) and understand tool calling and the Model Context Protocol (MCP)
• Evaluation engineering: you have built or operated eval harnesses (golden datasets, regression gates in CI, LLM-judge calibration) and can explain why they gate merges
• Post-training fluency: you have fine-tuned or post-trained a model, built the data pipeline behind one, or reproduced techniques from recent papers in production systems
• Daily, hands-on use of AI coding assistants as part of your own development workflow

Responsibilities

• One or more platform components end to end: model gateway (routing, failover, caching, per-line cost attribution), agent runtime and orchestration, evaluation harness, guardrail engine, tool and agent registry, or shared product services
• The training and adaptation loop: pipelines that turn production traces and evaluation verdicts into reinforcement learning and fine-tuning datasets, and the infrastructure to train, evaluate, and serve NewCo-tuned LLMs and SLMs behind the same gates as vendor models
• Reliability, latency, and cost of what you build; platform services carry baselines and you hold them
• The self-service surfaces product engineers use: your components ship with documentation, sane defaults, and no ticket queue
• Co-building with product teams: new shared services start embedded with a product line and graduate to platform services when proven

Skills

Python

Go

Rust

Kubernetes

IaC

LLM infra

Agent systems

Eval harness

Post-training

AI coding aids

Tools

LiteLLM

vLLM

LangGraph

pgvector

ClickHouse

OpenTelemetry

Grafana

Job description

Capgemini is seeking a production-focused Platform Engineer to build and operate AI infrastructure for regulated industries. You will own platform components like the model gateway, agent runtime, evaluation harness, and guardrail engine, collaborating with product teams to scale self-service services across multiple lines.

You will work on reinforcement learning readiness, model serving, and multi-tenant SaaS capabilities, contributing to a secure, observable, and cost-efficient cloud platform.