4 days ago
London, United KingdomStaff+
Responsibilities
- Lead the architecture, design, and implementation of enterprise AI and Generative AI solutions for regulated financial-services organizations.
- Define end-to-end architectures covering data ingestion, retrieval, orchestration, model serving, governance, monitoring, semantic layers, knowledge graphs, vector databases, and retrieval systems.
- Partner with engineering, data, security, risk, compliance, and executive stakeholders to operationalize AI solutions and move them into production.
- Develop AI evaluation, validation, monitoring, bias-detection, hallucination-management, output-quality, observability, and operational-resilience frameworks.
- Build reusable AI accelerators, reference architectures, design patterns, engineering blueprints, documentation, and implementation playbooks.
- Lead architecture reviews, technical governance forums, workshops, demonstrations, and executive architecture discussions.
- Mentor architects, engineers, and delivery teams and contribute to AI technology strategy, innovation, thought leadership, and practice development.
Requirements
- 10+ years of experience designing, architecting, and delivering enterprise data, analytics, AI, or machine-learning solutions.
- Proven experience deploying AI/ML solutions into production environments at enterprise scale.
- Hands-on architecture experience with Generative AI, large language models, retrieval-augmented generation, AI agents or agentic architectures, MLOps, and LLMOps.
- Experience with enterprise data platforms including Databricks, Snowflake, Azure Data Platform, AWS Data and AI Services, or Google Cloud AI Platforms.
- Experience designing knowledge-retrieval architectures using vector databases, semantic search, and metadata-driven architectures.
- Strong understanding of cloud architecture, microservices, APIs, and event-driven architectures.
- Experience establishing AI governance, model monitoring, evaluation frameworks, and observability practices.
- Experience in Financial Services, Banking, Insurance, Capital Markets, or another highly regulated industry.
- Strong knowledge of enterprise security, data privacy, risk management, and regulatory controls.
- Experience leading architecture reviews, technical governance forums, and executive stakeholder engagements.
- Ability to communicate complex technical concepts to technical and non-technical audiences.
- Desirable qualifications include product engineering or AI-platform development, Agentic AI frameworks, Model Context Protocol, OpenTelemetry, Responsible AI frameworks, AI-governance regulations, consulting and client-facing delivery, public speaking or technical publications, and financial-services or cloud-architecture certifications.
Benefits
- Hybrid working across company offices, client sites, and home; the role cannot be performed from home 100% of the time.
- Wellbeing support including Mental Health Champions and access to Thrive and Peppy wellbeing apps.
- Training and development opportunities including thinktanks, hackathons, 250,000 courses, and external certifications.
- Inclusive recruitment support through Capgemini’s Disability Confident Employer Level 2 commitment.
Tech Stack
Categories
Solutions Engineering
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.