Capgemini
Principal AI Platform Engineer
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About us
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
- 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 organisation
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
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What sets you apart
- 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 LangSmith and LangGraph Platform as the agent runtime and evaluation plane, model providers behind a swappable gateway seam, PostgreSQL with pgvector plus ClickHouse 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, OpenTelemetry and Grafana for observability, all on CNCF-conformant Kubernetes with Helm and Argo CD, deployable to any hyperscaler 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 tuned model (LLM or SLM) serves production traffic behind evaluation gates
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We are a Disability Confident Employer
Capgemini is proud to be a Disability Confident Employer (Level 2) under the UK Government’s Disability Confident scheme. As part of our commitment to inclusive recruitment, we will offer an interview to all candidates who:
- Declare they have a disability, and
- Meet the minimum essential criteria for the role.
Please opt in during the application process.
Make it real – what does it mean for you?
We realise a Total Reward package should be more than just compensation. At Capgemini we offer a range of core and flexible benefits and have a Peer Recognition Portal called Applaud.
You’d be joining an accredited Great Place to work for Wellbeing in 2024. Employee wellbeing is vitally important to us as an organisation. We see a healthy and happy workforce a critical component for us to achieve our organisational ambitions. To help support wellbeing we have trained ‘Mental Health Champions’ across each of our business areas, and we have invested in wellbeing apps such as Thrive and Peppy.
You will be empowered to explore, innovate, and progress. You will benefit from Capgemini’s ‘learning for life’ mindset, meaning you will have countless training and development opportunities from thinktanks to hackathons, and access to 250,000 courses with numerous external certifications from AWS, Microsoft, Harvard ManageMentor, Cybersecurity qualifications and much more.
Capgemini. Make it real.
Why you should consider Capgemini
Growing clients’ businesses while building a more sustainable, more inclusive future is a tough ask. When you join Capgemini, you’ll join a thriving company and become part of a collective of free-thinkers, entrepreneurs and industry experts. We find new ways technology can help us reimagine what’s possible. It’s why, together, we seek out opportunities that will transform the world’s leading businesses, and it’s how you’ll gain the experiences and connections you need to shape your future. By learning from each other every day, sharing knowledge, and always pushing yourself to do better, you’ll build the skills you want. You’ll use your skills to help our clients leverage technology to innovate and grow their business. So, it might not always be easy, but making the world a better place rarely is.
About Capgemini
Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organisations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2024 global revenues of €22.1 billion.
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