Senior AI Engineer – GenAI, RAG & Agents (Hybrid)

Capgemini; Greater London; Hybrid; GBP 110,000 - 150,000; Full time

Job summary

Capgemini is seeking a Senior AI Engineer in London to join the AI Engineering Team within the Digital Delivery pillar. The role blends office, client site, and home working, with a hybrid working model.

You will design and deploy enterprise-grade AI solutions including Generative AI, RAG, and AI agents, collaborating with architecture, security, and infrastructure teams. You will lead the adoption of best practices, model evaluation, observability, and governance across SDLC while translating

Qualifications

• 10+ years in technology delivery, software engineering, data science, or AI solution development.
• Minimum 2+ years delivering custom Generative AI on Microsoft Azure (preferred) or AWS.
• Experience building production-grade Generative AI apps (RAG, AI agents, prompts).
• Strong Python skills and scalable API/AI service development.
• Familiar with vector databases and LLMs; strong AI architecture understanding.

Responsibilities

• Collaborate with DXT teams to translate business requirements into AI solutions.
• Design, develop, and deliver scalable Generative AI capabilities aligned to business goals.
• Evaluate tech options for value, cost, risk, and long-term support.
• Follow SDLC, governance, testing, release management, and reporting.
• Promote engineering best practices, reusable frameworks, and security controls.

Skills

Generative AI

RAG

LLM integration

Python

Azure

Production APIs

Vector search

MLOps

Cloud (Azure/AWS)

Stakeholder management

Tools

Azure OpenAI Service

Azure AI Foundry

Pinecone/Weaviate/Milvus

Job description

Capgemini is seeking a Senior AI Engineer in London to join the AI Engineering Team within the Digital Delivery pillar. The role blends office, client site, and home working, with a hybrid working model.

You will design and deploy enterprise-grade AI solutions including Generative AI, RAG, and AI agents, collaborating with architecture, security, and infrastructure teams. You will lead the adoption of best practices, model evaluation, observability, and governance across SDLC while translating