13 days ago
Responsibilities
- Assess and implement MLOps frameworks, governance, and industry best practices.
- Design and deliver AI and ML systems from data preparation and model development through deployment, feature stores, model management, and monitoring.
- Build generative AI and agentic AI solutions using RAG pipelines, agentic workflows, and large language model orchestration across Azure, GCP, and AWS.
- Translate business and functional requirements into cloud-based data and AI architectures and technical blueprints.
- Embed safety, evaluation, assurance, ethical, explainable, and responsible AI mechanisms across the AI lifecycle.
- Guide multidisciplinary teams and collaborate with product managers, data scientists, and business stakeholders to deliver business value.
- Build client-ready demos and proofs of value, support proposals and technical deep-dives, and showcase delivery patterns.
- Develop reusable assets, frameworks, thought leadership, blog posts, and internal accelerators for AI engineering topics.
Requirements
- Experience in a major consulting firm or industry role with a consulting mindset and success in matrixed organisations.
- Experience working with technical and non-technical client sponsors to design requirements and build solutions collaboratively.
- Proven experience designing and implementing MLOps strategies and frameworks and delivering AI/ML 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 with at least one major cloud platform and its AI services, such as Azure, GCP, or AWS.
- Experience building and automating AI/ML pipelines with 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 and implement RAG pipelines, agentic workflows, MCP integrations, and integrations with LLM APIs such as OpenAI, Anthropic, or Hugging Face.
- Proficiency in CI/CD and containerisation tools including GitHub Actions, Azure DevOps, Docker, and Kubernetes.
- Familiarity with AI performance evaluation, prompt evaluation, A/B testing, quality assessment frameworks, lakehouse architectures, vector databases, relational and NoSQL stores, API gateways, and event streaming.
- Ability to demonstrate how scaling AI can unlock business value and impact.
- Applicants must have resided in the UK for the last five years.
Benefits
- Hybrid and flexible working arrangements with office bases in London, Manchester, or Glasgow.
- Flexible benefits options and a variable compensation element based on grade and company and personal performance.
- Wellbeing support including Mental Health Champions and Thrive and Peppy wellbeing apps.
- Opportunity to work on in-house innovation projects, major client engagements, and technology showcased at events and conferences.
- Diversity, inclusion, ethical business, and social-impact initiatives.
- The role may involve periods away from home at short notice, and employees must be flexible regarding assignment location.
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