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# GenAI Platform Engineer (Python, MCP & Agentic AI)

- **Company:** Capgemini
- **Location:** Hanover, NJ, United States
- **Salary:** $90,786.0 - $107,298.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, FactSet, AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, User Authentication, Cloud Computing, Continuous Integration, Python (Programming Language), Key Management, Search Technologies, Systems Integration, AI Infrastructure, Data Logging, Pinecone, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Generative AI, Backend, Agentic-AI, Git, Fastapi, Pgvector, Containerization, Kubernetes, Information Technology, Weaviate, Search Engines, Claude, Asynchronous Programming, Invoking Functions, Api Design, Model Context Protocol, Serverless Computing, OpenSearch, ChromaDB
- **Published:** October 26, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3467530452&tx=TT4239TTT&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelor's degree in Computer Science, Engineering, AI, or a related technical field (or equivalent experience).
 * Strong hands-on experience with Python backend development.
 * Proven experience building GenAI applications, LLM workflows, agentic systems, or AI-powered platforms.
 * Experience with MCP concepts and implementations, including tools, resources, servers, and clients.
 * Experience with LangChain, LangGraph, or similar orchestration frameworks.
 * Strong understanding of RAG, embeddings, vector search, retrieval quality, and grounding techniques.
 * Experience developing APIs using FastAPI, REST, and async Python.
 * Knowledge of Git, CI/CD, testing frameworks, and cloud deployment practices.
 * Experience with AWS and containerized applications.
 * Understanding of enterprise security, authentication, authorization, and secrets management.
 * Experience in banking, financial services, capital markets, or other regulated environments preferred., * Experience with AWS Bedrock, Claude models, and managed GenAI services.
 * Experience integrating FactSet, Bloomberg, S&P Capital IQ, Refinitiv, Fitch, or similar data providers.
 * Knowledge of OpenSearch, Pinecone, pgVector, Weaviate, Chroma, or other vector databases.
 * Familiarity with OpenTelemetry, observability, AI evaluation frameworks, and prompt management.
 * Understanding of AI governance, model risk management, data privacy, and regulatory controls.
 * Exposure to investment banking workflows including IPOs, ECM, DCM, M&A, and advisory processes.

## Description

We are seeking a highly skilled GenAI Platform Engineer to design, develop, and scale next-generation AI-powered solutions that support investment banking and financial services workflows. This role will focus on building enterprise-grade Generative AI platforms, agentic workflows, Model Context Protocol (MCP) integrations, Retrieval-Augmented Generation (RAG) capabilities, and secure backend services that enable business users to leverage AI solutions in a controlled, governed, and production-ready environment.

The ideal candidate will have deep expertise in Python, GenAI architectures, LLM orchestration frameworks, MCP implementations, API development, cloud-native services, and financial data integrations ., * Design, develop, and maintain Python-based GenAI services, agent workflows, MCP integrations, and reusable platform components.

 * Build MCP servers, clients, tools, resources, prompts, schemas, and authorization frameworks.
 * Develop agent orchestration workflows including tool calling, function calling, retrieval, workflow execution, and output generation.
 * Design and implement RAG pipelines covering ingestion, embeddings, retrieval, ranking, answer synthesis, and citations.
 * Integrate internal and external data sources, including financial data providers, SEC filings, enterprise repositories, web search, and banking platforms.
 * Develop secure, production-grade APIs and backend services using Python, FastAPI, and asynchronous programming.
 * Implement authentication, authorization, entitlement controls, logging, monitoring, testing, and model output validation.
 * Collaborate with business stakeholders, architects, cloud engineers, and governance teams to deliver production-ready AI solutions.
 * Create and maintain technical documentation, architecture designs, and operational procedures.