Job DescriptionRole Overview- 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.
Key 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.
Required Skills- 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.
Preferred Skills- 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.
Must-Have- 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.
The base compensation range for this role in the posted location is: 80,000 to 101,050
Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.
The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.
These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.
It is not typical for candidates to be hired at or near the top of the posted compensation range.
In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.
Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:
- 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
