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About us-

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.

What you will own

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

What you will need
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

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

The reference stack
The reference technology stack for this role is our supported paved road: self-hosted Lang Smith and Lang Graph Platform as the agent runtime and evaluation plane, model providers behind a swappable gateway seam, PostgreSQL with pg vector plus Click House and S3-compatible object storage as the data platform, Neo4j Enterprise as the semantic knowledge graph, an agent memory plane serving episodic and precedent memory over MCP, MCP-native connectors, Open Telemetry and Grafana for observability, all on CNCF-conformant Kubernetes with Helm and Argo CD, deployable to any hyper scaler or on-prem. A tool-for-tool match is not expected: analogous experience counts fully. If you have built and operated systems of this shape on comparable components (a different orchestration framework, graph engine, evaluation platform, or serving stack), you have what we are looking for.

How we work
Engineers write specs, harnesses, evals, and guardrails; AI agents execute the implementation loops. Review, not typing, is where engineering judgment goes.
Three human gates govern everything we ship: spec approval, merge, and release. Regulated code paths (money movement, authentication, cryptography, secrets) are always human-owned.
Small and senior by design. No separate QA function, no scrum masters; quality comes from evaluation gates and whole-team review rituals.
Domain experts (claims practitioners, payment scheme experts, clinicians) are full-time members of the product teams you will serve.

Success in year one
The first product line ships to its first enterprise client on platform services it chose to use, with platform cost attributed per line
Platform adoption is voluntary and measured; product teams' deployment frequency and change-failure rates improve after adoption
Bank or insurer model-risk teams accept the platform's evidence pack on first review
A written engineering strategy exists, is re-argued each quarter against frontier developments, and the first New Co-tuned model (LLM or SLM) serves production traffic behind evaluation gates