Capgemini

Gen AI / Agentic AI Developer

Capgemini • $80K — $106K *
Brooklyn, NY 11226In-Person
Enterprise Technology
Less than 5 years of experience
Today
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Job Overview by Ladders

Qualifications

  • 5-7 years of hands-on Python development experience
  • Familiarity with LLMs, RAG, embeddings, vector databases, and prompt engineering
  • Experience with agentic frameworks like LangChain, LlamaIndex, or Semantic Kernel
  • Knowledge of cloud services such as OpenAI, Azure, or AWS
  • Understanding of REST APIs and microservices architecture
  • Hands-on experience with Docker, Kubernetes, and CI/CD pipelines
  • Ability to handle both structured and unstructured data

Responsibilities

  • Develop and maintain GenAI applications utilizing LLMs and tool-calling workflows
  • Implement agentic frameworks for optimized AI solutions
  • Create multi-agent workflows for processing and validation
  • Build backend APIs and microservices for application integration
  • Coordinate AI agents with enterprise systems and databases
  • Streamline document ingestion and vector search pipelines
  • Deploy and monitor applications in cloud environments using CI/CD practices

Benefits

  • Flexible work arrangements
  • Comprehensive healthcare plan including mental health support
  • Financial wellness services including a 401(k) plan
  • Paid time off and holidays
  • Parental leave and family building support
  • Social well-being programs such as subsidized care services
  • Access to mentoring and development programs
  • Involvement in Employee Resource Groups
Full Job Description
Job Location - NYC NY / Charlotte NC (Day One Onsite - Hybrid)

Interview Process

  • Candidates must be based in or willing to travel to Chicago IL, New York City NY, Atlanta GA, or Charlotte NC, as the interview process includes an in-person round


About The Role

  • 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


Life At Capgemini

Capgemini supports all aspects of your well-being throughout the changing stages of your life and career. For eligible employees, we offer:
  • 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


Disclaimer

The primary focus is to help organizations design, develop, and optimize their data infrastructure and systems. They help organizations enhance data processes, and leverage data effe

ctively to drive business outcomes.

About Capgemini

Capgemini is a global leader in consulting, digital transformation, technology and engineering services. The company is headquartered in Paris, France and operates in over 50 countries. Capgemini provides a range of services including strategy and transformation, application services, technology services, and engineering services. The company serves clients in a variety of industries including automotive, consumer products, financial services, healthcare, and retail.
Learn more about Capgemini
Industry
Information Technology
Founded
1967
NASDAQ
CGEMY
* Ladders Estimates

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