9 days ago
Responsibilities
- Design and deliver scalable end-to-end AI and machine learning systems from data preparation and model development through deployment, model management, feature stores, and monitoring.
- Architect and implement generative AI and agentic AI solutions, including RAG pipelines, agentic workflows, MCP integrations, and LLM orchestration across Azure, GCP, and AWS.
- Embed safety, evaluation, assurance, ethical, explainable, and responsible AI mechanisms throughout the AI lifecycle.
- Lead multidisciplinary teams in executing complex client and business requirements.
- Collaborate with product managers, data scientists, business stakeholders, and technical and non-technical client sponsors to define requirements and deliver business value.
- Support business development through client-ready demos, proofs of value, proposals, technical deep-dives, and delivery-pattern showcases.
- Build reusable assets and frameworks, contribute to internal communities and best practices, and develop thought leadership and accelerators in AI engineering.
Requirements
- Experience in a major consulting firm or industry role with a consulting mindset and success in matrixed organisations.
- Experience designing and implementing MLOps strategies and frameworks and delivering AI and machine learning solutions at scale from concept to production.
- Deep understanding of generative AI and agentic AI, including RAG pipelines, embeddings, evaluation harnesses, and orchestration frameworks.
- Experience designing cloud-native data and AI architectures across Azure, GCP, AWS, and/or Databricks.
- Experience deploying and scaling AI solutions on at least one major cloud platform.
- Experience building and automating AI and machine learning pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow, or Google ADK.
- Hands-on experience with generative and agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, Autogen, or Google ADK.
- Ability to design RAG pipelines, agentic workflows, MCP integrations, and integrations with LLM APIs such as OpenAI, Anthropic, or Hugging Face.
- Proficiency in CI/CD and containerisation technologies including GitHub Actions, Azure DevOps, Docker, and Kubernetes.
- Familiarity with AI system evaluation, prompt evaluation, A/B testing, quality assessment frameworks, lakehouse architectures, vector databases, relational and NoSQL stores, API gateways, and event streaming is preferred.
- Applicants must have resided in the UK for the last five years.
Benefits
- Hybrid working and flexible working arrangements are available to UK employees.
- The role has an office base location but requires flexibility to work at assignment locations, including periods away from home at short notice.
- Flexible benefits options, a variable performance-dependent element, wellbeing apps including Thrive and Peppy, and access to Mental Health Champions are provided.
- The company highlights diversity and inclusion initiatives, work-life balance support, and opportunities to work on emerging technology and social-impact projects.
Tech Stack
Categories
About Capgemini
Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion. Make it real | www.capgemini.com