Experience:

• Experienced Professionals
• 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

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

Additional responsibilities:

• Clearly explain at least one end-to-end GenAI / Agentic AI project, including problem statement, architecture, tools used, deployment approach, evaluation method, and business impact

Nice to have:

• 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

More skills:

GenAI, Agentic AI, agents, tool-calling workflows, LangGraph, AutoGen, CrewAI, Semantic Kernel, Python, FastAPI, Flask, REST APIs, microservices, AWS Bedrock, Llama, semantic search, OpenSearch, Pinecone, FAISS, Chroma, Weaviate, Milvus, Azure AI Search, pgvector, Docker, Kubernetes, CI/CD, cloud platforms, prompt/version management, model evaluation, monitoring, logging, cost tracking, structured data, unstructured data, PDFs, documents, APIs, databases, knowledge bases

Other:

• Base compensation range for the posted location: 72,000 - 97,000
• Additional compensation may include variable incentives, bonuses, or commissions, depending on the position and applicable laws
• Comprehensive, non-negotiable benefits package for regular, full-time employees
• Paid time off based on employee grade: vacation
• Medical, dental, and vision coverage or provincial healthcare coordination in Canada
• Retirement savings plans such as 401(k) in the U.S. or RRSP in Canada
• Life and disability insurance
• Employee assistance programs
• Equal Opportunity Employer encouraging inclusion in the workplace
• Participates in the Partnership Accreditation in Indigenous Relations program
• Reasonable accommodation during the recruitment process
• Capgemini may capture your image during the interview process for verification
• Brand: Capgemini
• Professional Community: Data & AI
• Contract Type: Permanent
• Ref. code: 500688
• Posted on: Jul 20, 2026
• Experience Level: Experienced Professionals
• Nearest Major Market: Manhattan
• Nearest Secondary Market: New York City
• Global business and technology transformation partner with strong capabilities in AI, generative AI, cloud and data