Senior AI Platform Engineer

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

New Co is a new AI-native product organization within Capgemini Financial Services. We build products, not projects: software for insurance claims, payment operations, and health operations, sold to banks, insurers, and health plans. Three product lines run on one shared platform, built by a deliberately small, senior team. Our engineering model is agentic: engineers author the specifications, tooling, evaluation suites, and guardrails, and AI agents do most of the implementation.

Humans own every consequential decision, and in our regulated domains some decisions are human-only by design.

Three product lines, one platform. You will own the platform that Claims, Payments, and Health run on: the agentic AI floor (model gateway, agent runtime, evaluation infrastructure, guardrails) and the shared product services around it (case management and work queues, integration connectors, multi-tenancy, metering). You run the platform as a product whose customers are our product teams, and you are its first and most senior engineer-leader.

Every hour of claims handling or payment processing our products automate rests on infrastructure your group builds.

The strategic vision, roadmap, and end-to-end lifecycle of the platform: from the model gateway and agent runtime to shared workflow, tenancy, and metering services

A competitive engineering strategy at the frontier: you track research and model releases as they land, decide what the platform adopts versus builds, and keep our capability curve ahead of what clients could assemble themselves

The closed improvement loops: production signals and evaluation verdicts feed reinforcement learning and fine-tuning pipelines that produce our own LLMs and SLMs; product loops run automated end to end, with humans holding the gates

Build-vs-buy decisions across open-source and commercial AI infrastructure, and the boundary between what the platform provides and what product lines build themselves

A disciplined operating model: a published capacity split between product-team requests, platform quality, and strategic initiatives; services graduate to self-service only when they are ready

Platform adoption outcomes: your group is measured by the delivery metrics of its consumers, not its own output

Compliance posture of the platform in regulated environments: model risk documentation, audit trails, and responsible-AI practices that bank and insurer risk teams can examine; no AI capability ships ungoverned or unevaluated, including the models we train ourselves

Hiring and growing the platform group, and the engineering standards it sets for the whole organization

A track record leading platform or infrastructure teams that ran production systems for multiple product teams, with accountability for adoption, not just delivery

Hands-on credibility in modern AI infrastructure: LLM inference and serving, model gateways, vector search, guardrails, and evaluation systems

Frontier research fluency: you read post-training, reinforcement learning, and agentic-systems work as it lands and can turn it into engineering strategy; an engineer who reads research, not a researcher at engineering distance

Cloud platform depth (AWS, Azure, or GCP) with Kubernetes and infrastructure-as-code at production scale

Experience…