GenAI Solutions Engineer: LLM Apps & Agentic Workflows

Capgemini; New York (NY); On-site; USD 72,000 - 97,000; Full time

Benefits offered by this job

Paid time off

Medical coverage

Retirement plans

Job summary

Capgemini is seeking a hands-on GenAI/Agentic AI Developer to build LLM-powered applications, RAG solutions, and agentic AI workflows for enterprise use cases.

You will design multi-agent architectures, develop Python backends, and integrate AI agents with enterprise systems, cloud services, and data sources. The role emphasizes operational excellence with Docker, Kubernetes, and CI/CD.

Qualifications

• Hands-on Python development experience.
• Experience with large language models and GenAI tooling.
• Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or LlamaIndex).
• Understanding of RAG, embeddings, vector databases and prompt engineering.
• Experience with vector stores (OpenSearch, Pinecone, FAISS, Chroma, Weaviate, Milvus, pgvector).
• Knowledge of REST APIs, cloud deployment, Docker, CI/CD, and software engineering best practices.

Responsibilities

• Build GenAI applications using LLMs, RAG, agents, and tool‑calling workflows.
• Develop agentic solutions with LangChain/LangGraph/AutoGen etc.
• Design multi-agent workflows (planner, retriever, executor, validator, human‑in‑the‑loop).
• Build backend APIs using Python, FastAPI, Flask, REST, and microservices.
• Integrate AI agents with enterprise systems, databases, APIs, and clouds.
• Implement document ingestion, embeddings, vector search, and retrieval pipelines.
• Deploy and monitor GenAI apps using Docker, Kubernetes, CI/CD and cloud platforms.
• Support LLMOps including prompts, model eval, monitoring, logging, and cost tracking.

Skills

Python development

GenAI

Agentic AI

LangChain/LangGraph

REST APIs

Tools

Docker

Kubernetes

FastAPI

Cloud services

Job description

Capgemini is seeking a hands-on GenAI/Agentic AI Developer to build LLM-powered applications, RAG solutions, and agentic AI workflows for enterprise use cases.

You will design multi-agent architectures, develop Python backends, and integrate AI agents with enterprise systems, cloud services, and data sources. The role emphasizes operational excellence with Docker, Kubernetes, and CI/CD.