1 day ago
Manchester, United Kingdom +2 moreSenior
Responsibilities
- Assess and implement robust MLOps frameworks, governance, and industry best practices.
- Design and deliver end-to-end AI/ML systems spanning data preparation, model development, deployment, feature stores, model management, and monitoring.
- Architect and implement generative AI solutions, including RAG pipelines, agentic workflows, and LLM orchestration across Azure, GCP, and AWS.
- Deliver solutions using generative AI and agentic frameworks such as ADK, LangGraph, Microsoft Agent Framework, and LlamaIndex.
- Translate business requirements into data and AI architectures, technical blueprints, and executable delivery plans.
- Embed safety, evaluation, assurance, explainability, and responsible AI mechanisms throughout the AI lifecycle.
- Collaborate with product managers, data scientists, business stakeholders, and multidisciplinary delivery teams.
- Build client-ready demonstrations and proofs of value, support proposals and technical deep-dives, and showcase delivery patterns.
- Develop reusable assets, frameworks, thought leadership, and internal accelerators for AI engineering.
Requirements
- Experience in a major consulting firm or an industry role with a consulting mindset and success in a matrixed organization.
- Experience working with technical and non-technical client sponsors to define requirements and deliver solutions.
- Proven experience designing 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 using Azure, GCP, AWS, and/or Databricks.
- Experience with at least one major cloud platform and its AI services, such as Azure Foundry, Azure AI Studio, Azure OpenAI, AKS, GCP Vertex AI, Cloud Run, AWS Bedrock, or SageMaker.
- Experience building and automating AI/ML pipelines using 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 with GitHub Actions, Azure DevOps, Docker, and Kubernetes.
- Familiarity with 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 with flexible working arrangements and an office base in London, Manchester, or Glasgow.
- Flexible benefits options, wellbeing support including Mental Health Champions, Thrive, and Peppy apps, and diversity and inclusion initiatives.
- The role may require full flexibility regarding assignment locations and periods away from home at short notice.
- Opportunities to work on in-house innovation projects, attend major events and conferences, and contribute to internal communities and thought leadership.
Tech Stack
Categories
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.