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# GenAI Application Engineer

- **Company:** Capgemini
- **Location:** New York, NY, United States
- **Salary:** $86,129.0 - $127,189.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Databases, Continuous Integration, Data Governance, Data Integration, Python (Programming Language), Knowledge-Based Systems, Language Modeling, Search Technologies, Secure Coding, Software Deployment, Software Engineering, Large Language Models, Generative AI, Restful APIs, Devsecops
- **Published:** May 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=241913635dfbf4ea

## About the Role

Do you have experience in Python?, * Expertise in Python

 * LLMs exposure to Open AI or Anthropic
 * Agentic framework like Lang Chain or Lang graph
 * Develop RAG pipelines embeddings vector search and knowledge integrations

## Description

Key Skills: Python LLMs such as OpenAI Anthropic Copilot CICD containers LangChain Semantic Kernell, As a GenAI Application Engineer, you will design, develop, and deploy intelligent applications leveraging advanced LLMs and generative AI technologies. You will deliver end-to-end solutions, implement RAG architectures, integrate diverse data sources, and build high-quality prototypes for next-generation Advisor Assistance tools.

Primary Responsibilities

 * Build and integrate applications using LLMs multimodal models and GenAI frameworks
 * Develop RAG pipelines embeddings vector search and knowledge integrations
 * Develop AI automation intelligent tooling and AIassisted engineering practices
 * Build production ready prototypes that enhance and streamline workflows
 * Develop RAG Architectures Knowledge Systems
 * Integrate structured and unstructured content from internal and external systems
 * Optimize retrieval performance latency relevance and RAG evaluation metrics
 * Data Integration Ingestion
 * Gather and consolidate data from diverse sources such as REST APIs databases enterprise systems and document repositories
 * LLM Driven Application Development
 * Implement system prompts agent architectures multimodal workflows and AI orchestrated tools
 * Prototype conversational interfaces advisor systems and AI assistants
 * AI Engineering Automation
 * Develop intelligent tooling agents and automation to augment engineering workflows
 * Embed Gen AI features into existing processes to enhance productivity and decision making
 * Prototype Delivery Experimentation
 * Design and deliver production ready prototypes and proofs of concept demonstrating feasibility and business value
 * Iterate quickly based on user and stakeholder feedback
 * DevOps Security and Operationalization
 * Package and deploy applications using containers APIs and CICD pipelines
 * Apply monitoring observability testing and secure coding practices
 * Ensure compliance with Responsible AI privacy and data governance standards


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