GEN AI Lead
Capgemini
Job Overview
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Job Description
About Capgemini
Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
Job Summary: GEN AI Lead
In this role, you will lead the design, delivery, and adoption of enterprise grade GenAI data platforms, with a strong emphasis on market data. You will own and ship agentic GenAI data workflows, including retrieval augmented generation (RAG), tool calling, and orchestration, while embedding governance, controls, and quality from day one. You will design and scale AI agent capabilities across Azure and AWS, leveraging Microsoft 365 Copilot and Copilot Studio, and integrating leading OpenAI and Anthropic large language models (LLMs) with privacy, security, and regulatory controls built in. The role has deep responsibility for market data platforms.
Key Responsibilities:
- Lead the design and delivery of GenAI data architectures, including agentic workflows, RAG pipelines, prompt orchestration, and tool calling frameworks.
- Own the strategy and integration of market data platforms, ensuring high quality ingestion, normalization, enrichment, and access for analytics and GenAI use cases.
- Design and oversee scalable GenAI and machine learning data pipelines, supporting structured and unstructured data across the full AI lifecycle.
- Establish and embed data quality, lineage, privacy, and compliance controls to ensure responsible AI use and regulatory alignment.
- Partner with business, product, engineering, and data science teams to identify high impact GenAI use cases and translate them into production grade solutions.
- Define best practices for model lifecycle management, evaluation metrics, MCP integration, and continuous improvement of GenAI systems.
- Act as a trusted senior advisor to Markets and Investment Banking stakeholders.
- Build, lead, and mentor high performing teams of data engineers and GenAI practitioners.
Preferred Skills:
- Experience designing and overseeing GenAI and ML data pipelines, integrating structured and unstructured data for model deployment and evaluation.
- Strong expertise in enterprise GenAI and LLM platforms, including prompt engineering pipelines, vector databases, and retrieval augmented generation (RAG).
- Proven ability to establish data governance, privacy, and compliance frameworks in regulated financial services environments.
- Experience partnering with business and engineering teams to deliver production grade GenAI solutions.
- Strong leadership skills, with experience setting standards and best practices across data, analytics, and AI teams.
Compensation & Benefits:
The base compensation range for this GEN AI Lead role is 103,330 to 128,656 USD annually. Actual compensation may vary based on location, qualifications, experience, and market conditions. Capgemini offers a comprehensive, non-negotiable benefits package for full-time employees, which may include paid time off (12-25 days vacation, holidays, personal, sick leave), medical, dental, and vision coverage, retirement savings plans (e.g., 401(k)), life and disability insurance, and employee assistance programs. These benefits are subject to local policy and eligibility.
Key skills/competency:
- Generative AI
- Data Platforms
- Retrieval Augmented Generation (RAG)
- Large Language Models (LLMs)
- Data Governance
- Machine Learning
- Prompt Engineering
- Azure
- AWS
- Market Data
How to Get Hired at Capgemini
- Research Capgemini's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Customize your resume: Highlight your expertise in GenAI, ML, data platforms, and cloud technologies.
- Showcase relevant experience: Emphasize projects involving agentic AI, RAG, LLMs, and market data in regulated environments.
- Prepare for technical deep-dives: Focus on Azure, AWS AI services, OpenAI/Anthropic LLMs, and data pipeline design.
- Demonstrate leadership capabilities: Be ready to discuss team building, mentorship, and stakeholder advisory roles.
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