GenAI / Agentic AI Developer

We are looking for a hands-on Gen AI / Agentic AI Developer to build LLM-powered applications, RAG solutions, and agentic AI workflows for enterprise use cases.

Key Responsibilities:

Build Gen AI applications using LLMs, RAG, agents, and tool-calling workflows.

Develop agentic solutions using Lang Chain, Lang Graph, Auto Gen, Crew AI, Semantic Kernel, or Llama Index.

Design and implement multi-agent workflows such as planner, retriever, executor, validator, and human-in-the-loop agents.

Build backend APIs using Python, Fast API, 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 Gen AI applications using Docker, Kubernetes, CI/CD, and cloud platforms.

Support LLM Ops including prompt/version management, model evaluation, monitoring, logging, and cost tracking.

Required Skills:

Strong hands-on experience in Python development.

Experience with Open AI, Azure Open AI, AWS Bedrock, Anthropic Claude, Gemini, Llama, or Mistral.

Hands-on experience with at least one agentic framework: Lang Graph, Lang Chain, Auto Gen, Crew AI, Semantic Kernel, or Llama Index.

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 LLM Ops tools such as Lang Smith, ML flow, Phoenix, Ragas, TruLens, Arize, or Open Telemetry.

Experience with AWS Bedrock/Sage Maker, Azure Open AI/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.