Gen AI Engineer

Capgemini • New York, NY

1d • $80,420 - $106,050 • Hybrid

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Requirements

• Strong hands-on experience in Python development.
• Experience with OpenAI, Azure OpenAI, AWS Bedrock, Anthropic Claude, Gemini, Llama, or Mistral.
• Hands-on experience with at least one agentic framework: LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or LlamaIndex.
• Good understanding of RAG, embeddings, vector databases, semantic search, and prompt engineering.
• Experience with vector stores such as OpenSearch, Pinecone, FAISS, Chroma, Weaviate, Milvus, Azure AI Search, or pgvector.
• Knowledge of REST APIs, cloud deployment, Docker, CI/CD, and software engineering best practices.
• Ability to work with structured and unstructured data including PDFs, documents, APIs, databases, and knowledge bases.

Nice To Haves

• Experience with multi-agent orchestration, tool calling, memory, planning, reflection, and evaluation.
• Exposure to MCP, Graph RAG, Neo4j, knowledge graphs, or entity extraction.
• Knowledge of LLMOps tools such as LangSmith, MLflow, Phoenix, Ragas, TruLens, Arize, or OpenTelemetry.
• Experience with AWS Bedrock/SageMaker, Azure OpenAI/AI Search, or GCP Vertex AI.
• Understanding of AI guardrails, prompt injection prevention, PII masking, access control, and responsible AI.

Responsibilities

• Build GenAI applications using LLMs, RAG, agents, and tool-calling workflows.
• Develop agentic solutions using LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or LlamaIndex.
• Design and implement multi-agent workflows such as planner, retriever, executor, validator, and human-in-the-loop agents.
• Build backend APIs using Python, FastAPI, Flask, REST APIs, and microservices.
• Integrate AI agents with enterprise systems, databases, APIs, document repositories, and cloud services.
• Implement document ingestion, embeddings, vector search, reranking, and retrieval pipelines.
• Deploy and monitor GenAI applications using Docker, Kubernetes, CI/CD, and cloud platforms.
• Support LLMOps including prompt/version management, model evaluation, monitoring, logging, and cost tracking.

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs
• Other benefits as provided by local policy and eligibility

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed