Gen AI / Agentic AI Developer

Capgemini • Columbia, SC

1d • $80,420 - $106,050 • Hybrid

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.

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
• Medical, dental, and vision coverage
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs

What This Job Offers

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