Senior Python/GenAI Engineer

Capgemini•New York, NY

2d•$105,000 - $115,000•Onsite

About The Position

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.

Requirements

Strong hands-on experience in Python for backend ML or GenAI application development
Proven experience building GenAI applications LLM workflows agentic systems or AI enabled production services
Solid understanding of MCP concepts and practical implementation patterns for tools resources prompts servers and clients
Experience with LLM orchestration frameworks such as LangChain LangGraph or similar
Strong understanding of RAG vector search embeddings retrieval quality citations and grounding patterns
Experience developing APIs and backend services using FastAPI REST APIs async Python and service-to-service integration patterns
Working knowledge of Git modern version control practices CICD workflows and test automation
Experience deploying or integrating containerized services in a cloud environment preferably AWS
Good understanding of authentication authorization secrets handling entitlements and enterprise security patterns
Ability to independently debug complex distributed systems across APIs data flows cloud services prompts and model outputs
Prior experience in banking financial services capital markets or other regulated technology environments
Bachelor degree in a quantitative or technical discipline such as Computer Science Engineering AI or equivalent experience

Responsibilities

Design develop enhance and maintain Python based GenAI services agent workflows MCP integrations and reusable platform capabilities
Build MCP servers clients tools resources prompts schemas and authorization patterns for internal and third-party systems
Implement agent orchestration flows including tool calling function calling workflow execution retrieval and output generation
Integrate internal and external data sources such as financial data providers SEC filings web search enterprise repositories and banking data services into agent workflows
Design and implement RAG pipelines covering ingestion chunking embeddings retrieval ranking answer synthesis and citations
Develop production grade backend services and APIs using Python FastAPI async programming patterns and secure service integration practices
Implement authentication authorization entitlement checks and secure access patterns for user specific consumption of MCP enabled services
Perform testing debugging prompt evaluation model output validation logging monitoring and operational telemetry for GenAI components
Work closely with product owners' business analysts architects cloud engineers governance teams and bankers to convert requirements into production ready AI capabilities
Maintain clear documentation of technical design assumptions interfaces limitations failure modes and operating considerations