Gen AI / Agentic AI Developer
About The Position
We are looking for a hands-on GenAI / Agentic AI Developer to build LLM-powered applications, RAG solutions, and agentic AI workflows for enterprise use cases. Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
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.
- Candidate should be able to 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 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
- Flexible work
- Healthcare including dental, vision, mental health, and well-being programs
- Financial well-being programs such as 401(k) and Employee Share Ownership Plan
- Paid time off and paid holidays
- Paid parental leave
- Family building benefits like adoption assistance, surrogacy, and cryopreservation
- Social well-being benefits like subsidized back-up child/elder care and tutoring
- Mentoring, coaching and learning programs
- Employee Resource Groups
- Disaster Relief
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
