> Markdown version of [/jobs/ext/2149047-senior-python-genai-engineer](https://www.wearedevelopers.com/jobs/ext/2149047-senior-python-genai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Python/GenAI Engineer - **Company:** Capgemini - **Location:** Hanover, NJ, United States - **Experience:** Expert - **Salary:** $105,000.0 - $115,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Cloud Computing, Computer Engineering, Continuous Integration, Data as a Services, Software Debugging, Distributed Systems, Python (Programming Language), Cloud Services, Search Technologies, Software Engineering, Data Streaming, Systems Integration, Data Logging, Large Language Models, Generative AI, Backend, Git, Fastapi, Search Engines, Api Design, Restful APIs, Software Version Control - **Published:** August 20, 2026 - **Apply:** https://www.capgemini.com/jobs/538532-en_US_SAPBTP/x/ ## About the Role To be successful in this role you should have * 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 ## Description * 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 ## Related Videos - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) - [Building and Deploying Multi-Agent Systems with ADK and Vertex AI](https://www.wearedevelopers.com/videos/1918-building-and-deploying-multi-agent-systems-with-adk-and-vertex-ai) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)