AI platform engineer in financial services

[New Co] is a new AI-native product organisation within Capgemini Financial Services. We build products, not projects: software for insurance claims and payment operations, sold to banks and insurers. Two 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.

One or more platform components end to end: model gateway (routing, failover, caching, per-line cost attribution), agent runtime and orchestration, evaluation harness, guardrail engine, tool and agent registry, or shared product services The training and adaptation loop: pipelines that turn production traces and evaluation verdicts into reinforcement learning and fine-tuning datasets, and the infrastructure to train, evaluate, and serve New Co-tuned LLMs and SLMs behind the same gates as vendor models Reliability, latency, and cost of what you build;

platform services carry baselines and you hold them The self-service surfaces product engineers use: your components ship with documentation, sane defaults, and no ticket queue Co-building with product teams: new shared services start embedded with a product line and graduate to platform services when proven

Strong production engineering:
Python plus one systems language (Go or Rust welcome), Kubernetes, infrastructure-as-code, and one major cloud Hands‑on experience with LLM infrastructure: a model gateway pattern (such as LiteLLM or in‑house), model serving (such as vLLM or managed endpoints), and vector or retrieval systems Agent‑systems experience: you have built with an agent orchestration framework (such as Lang Graph or first‑party agent SDKs) and understand tool calling and the Model Context Protocol (MCP) Evaluation engineering: you have built or operated eval harnesses (golden datasets, regression gates in CI, LLM‑judge calibration) and can explain why they gate merges Post‑training fluency: you have fine‑tuned or post‑trained a model, built the data pipeline behind one, or reproduced techniques from recent papers in production systems Daily, hands‑on use of AI coding assistants as part of your own development workflow Будет плюсом:
Guardrail‑engine or AI‑observability experience (NeMo Guardrails, Open Telemetry GenAI conventions, Lang Smith, Braintrust, or equivalent);
Reinforcement learning infrastructure (reward modelling, RLHF or GRPO‑class pipelines) or SLM distillation experience;
Multi‑tenant SaaS infrastructure: tenancy isolation, metering, usage‑based billing;
Financial services engineering (banks, insurers, or payment providers) is the ideal background; other regulated‑industry or security engineering depth also counts;
Open‑source contributions or a portfolio of deployed agents and eval suites (we would rather see this than a CV)

At Capgemini we offer 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. 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 think tanks to hackathons, and access to 250,000 courses with numerous external certifications from AWS, Microsoft, Harvard Manage Mentor, Cybersecurity qualifications and much more.