Senior AI Platform Engineer - LLM & MLOps Architect

Capgemini Invent; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Formal training by industry experts

Certifications across cloud and AI/ML

Les Fontaines training environment

Monthly team showcases and events

Job summary

Capgemini Invent invites an AI Platform Engineer to join the AI Platform Engineering practice in the UK. You will design, build and operate the infrastructure for enterprise AI and Generative AI workloads, spanning GPU compute, container platforms, model serving and MLOps tooling, across hybrid cloud environments.

Expect to lead architecture reviews, coach junior engineers, collaborate with clients and internal teams, and continuously evolve the platform while maintaining security and governance

Qualifications

• Must hold active SC clearance or be eligible and willing to gain it.
• Hands-on experience with AI/GenAI platform engineering and cloud infrastructure.
• Strong consulting, client-facing skills and ability to lead workshops.

Responsibilities

• Be a senior or lead engineer on client AI platform engagements.
• Architect and deploy AI-ready infrastructure across multi-cloud and on-prem environments.
• Build and operate core AI platform components: model serving, gateway, and orchestration.
• Implement MLOps/LLMOps pipelines using IaC, GitOps, CI/CD.
• Establish observability, security, and governance for AI systems.
• Lead client workshops, architecture reviews, andProvide mentoring to colleagues.

Skills

AI Platform Engineering

Kubernetes/OpenShift

Cloud-native infra

Terraform / IaC

GitOps / CI/CD

Python / Go / Rust

Observability & governance

Security clearance (SC)

Tools

Kubernetes/OpenShift

Terraform

GitOps

CI/CD pipelines

OpenTelemetry

Python

Job description

Capgemini Invent invites an AI Platform Engineer to join the AI Platform Engineering practice in the UK. You will design, build and operate the infrastructure for enterprise AI and Generative AI workloads, spanning GPU compute, container platforms, model serving and MLOps tooling, across hybrid cloud environments.

Expect to lead architecture reviews, coach junior engineers, collaborate with clients and internal teams, and continuously evolve the platform while maintaining security and governance