Principal AI Platform Engineer

Capgemini•Atlanta, GA

4d•$141,546 - $203,155•Remote

About The Position

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. The role 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.

Requirements

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 delivering in a regulated industry, ideally financial services, or demonstrable fluency in what model-risk and security review requires of a platform
A platform-as-product mindset: you can talk about golden paths, voluntary adoption, and developer research as naturally as architecture
Daily, hands-on use of AI coding assistants in your own work

Nice To Haves

You have owned both an AI platform floor and shared business services (workflow, tenancy, billing/metering) in one charter
You have taken a model through post-training (RLHF, RLAIF, fine-tuning, or distillation to smaller models) into production
Published or open-source work in agent infrastructure or evaluation tooling
Cost management (FinOps) experience for LLM workloads
Financial services domain depth: you have shipped production systems for banks, insurers, or payment providers

Responsibilities

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