GenAI / Agentic AI Developer

Capgemini • New York, NY

5d • $105,000 - $115,000

About The Position

We are looking for a hands-on Gen AI / Agentic AI Developer to build LLM-powered applications, RAG solutions, and agentic AI workflows for enterprise use cases.

Requirements

• Strong hands-on experience in Python development.
• Experience with Open AI, Azure Open AI, AWS Bedrock, Anthropic Claude, Gemini, Llama, or Mistral.
• Hands-on experience with at least one agentic framework: Lang Graph, Lang Chain, Auto Gen, Crew AI, Semantic Kernel, or Llama Index.
• 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 LLM Ops tools such as Lang Smith, ML flow, Phoenix, Ragas, TruLens, Arize, or Open Telemetry.
• Experience with AWS Bedrock/Sage Maker, Azure Open AI/AI Search, or GCP Vertex AI.
• Understanding of AI guardrails, prompt injection prevention, PII masking, access control, and responsible AI.

Responsibilities

• Build Gen AI applications using LLMs, RAG, agents, and tool-calling workflows.
• Develop agentic solutions using Lang Chain, Lang Graph, Auto Gen, Crew AI, Semantic Kernel, or Llama Index.
• Design and implement multi-agent workflows such as planner, retriever, executor, validator, and human-in-the-loop agents.
• Build backend APIs using Python, Fast API, 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 Gen AI applications using Docker, Kubernetes, CI/CD, and cloud platforms.
• Support LLM Ops 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