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# Senior AI Engineer

- **Company:** Capgemini
- **Location:** Atlanta, GA, United States
- **Experience:** Expert
- **Salary:** $73,150.0 - $174,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Application Integration Architecture, User Authentication, Databases, Software Debugging, Python (Programming Language), Node.Js, Software Engineering, Systems Integration, TypeScript, Data Logging, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Generative AI, AI Platforms, Virtual Agents
- **Published:** September 3, 2026
- **Apply:** https://www.capgemini.com/jobs/544881-en_US_SAPBTP/x/

## About the Role

* 5+ years of software development experience with Python and/or TypeScript/Node.js.
 * Hands-on experience building LLM/Generative AI applications.
 * Experience with LangChain, LangGraph, Semantic Kernel, or similar frameworks.
 * Experience with AI observability, evaluation frameworks, and prompt optimization.
 * Strong understanding of MCP (Model Context Protocol), AI agents, and tool calling.
 * Experience with Claude, OpenAI, or similar LLM platforms.
 * Experience integrating APIs, databases, and enterprise applications.
 * Knowledge of RAG, vector databases, embeddings, and prompt engineering.
 * Strong debugging, problem-solving, and communication skills.

Good to have skills

 * Knowledge of security, authentication, and access control for AI applications.
 * Experience deploying production-grade AI solutions.

## Description

We are seeking a hands-on AI Engineer to enhance and optimize our existing AI platform built on Claude Sonnet and Model Context Protocol (MCP). The role focuses on improving AI agents, MCP tools, enterprise integrations, response quality, performance, security, and scalability.

Your role

 * Enhance and maintain MCP-based AI applications and agents.
 * Develop and improve MCP servers, tools, and integrations.
 * Integrate AI solutions with enterprise systems, APIs, databases, and knowledge repositories.
 * Improve prompt engineering, context management, and response accuracy.
 * Build and optimize RAG (Retrieval-Augmented Generation) solutions.
 * Implement monitoring, logging, security controls, and performance improvements.
 * Troubleshoot AI agent behavior, tool execution, and platform issues.
 * Support deployment, testing, and continuous enhancement of AI capabilities.


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