Description

Lead the design and development of RAG-based and Agentic AI applications, architecting end-to-end Generative AI solutions.

This role is on-site.

Responsibilities

• Architect end-to-end Generative AI solutions leveraging LLMs, vector databases, and knowledge retrieval systems.
• Develop autonomous AI agents capable of planning, reasoning, tool usage, and multi-step workflow execution.
• Build and optimize AI pipelines for document ingestion, embedding generation, indexing, and retrieval.
• Evaluate and fine-tune foundation models for domain-specific use cases and implement prompt engineering strategies.
• Mentor AI engineers, drive technical decision-making, and establish AI governance and security practices.

Required Skills

• 10+ years of experience with Python and AI/ML development.
• Hands-on expertise in RAG architectures and knowledge retrieval systems.
• Experience with Agentic AI frameworks: LangChain, LangGraph, AutoGen, CrewAI.
• Proficiency with LLMs including GPT-4, Claude, Llama, and Gemini.
• Strong knowledge of vector databases such as Pinecone, Weaviate, Milvus, or Chroma.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Strong understanding of REST APIs, microservices, distributed systems, and MLOps.
• Experience with CI/CD pipelines and production AI deployments.

Preferred Skills

• Experience with fine-tuning LLMs and model evaluation frameworks.
• Familiarity with Graph RAG, Knowledge Graphs, and Multi-Agent Systems.

Key Skills

Education

Any Graduate