AI platform engineer in financial services

Capgemini; Greater London; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Applaud recognition portal

Great Place to Work wellbeing program

Mental Health Champions

Learning for life: courses and certs

Job summary

Capgemini's NewCo, an AI-native product organisation within Capgemini Financial Services, builds platform-focused software for insurance claims and payments with a small, senior team. Engineers author specifications, tooling, evaluation suites, and guardrails, while AI agents handle much of the implementation.

As a platform engineer you will own end-to-end components, from model gateway and orchestration to evaluation harnesses and guardrails, and you will work with product teams to embed new

Qualifications

• Strong production engineering: Python and a second systems language; Kubernetes, IaC and cloud.
• Hands-on experience with LLM infrastructure: model gateway, model serving, vector/retrieval systems.
• Experience with agent orchestration frameworks and tool calling concepts.
• Familiarity with eval harnesses and CI gating for model quality.

Responsibilities

• One or more platform components end to end: model gateway, agent runtime/orchestration, evaluation harness, or guardrail engine.
• Develop training/adaptation loops: pipelines turning production traces into RLHF/fine-tuning data.
• Ensure reliability, latency, and cost efficiency of platform services with solid docs.
• Create self-service surfaces for product engineers with sane defaults and documentation.

Skills

Python

Go

Rust

Kubernetes

IaC

Cloud

LLM infra

Agent frameworks

Tools

LangGraph

MCP

Job description

Описание:

[NewCo] 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.

Задачи:

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 NewCo-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

Требования:

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 LangGraph 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, OpenTelemetry GenAI conventions, LangSmith, 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)

Условия:

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 thinktanks to hackathons, and access to 250,000 courses with numerous external certifications from AWS, Microsoft, Harvard ManageMentor, Cybersecurity qualifications and much more.