Principal AI Platform Engineer

Principal 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.

Job Description

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

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

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

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 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

The base compensation range for this role in the posted location is 141546 - 203155

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range.

In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.

Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave

• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)

• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)

• Life and disability insurance

• Employee assistance programs

• Other benefits as provided by local policy and eligibility

Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini’s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.

Disclaimers

Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.

This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.

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Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.

Ref. code: 525660

Posted on: Aug 4, 2026

Experience Level: Experienced Professionals

Contract Type: Permanent

Location:

Atlanta, GA, US

Brand: Capgemini

Professional Community: Data & AI

Capgemini is an Equal Opportunity Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.