Responsibilities :

- Architect and implement enterprise-scale AI/ML solutions, including Generative AI, Agentic systems, and LLM applications ( including fine-tuning).

- Design and deploy intelligent AI agents using frameworks like LangChain and LangGraph to enhance user experience and operational efficiency.

- Build and optimize NLP/document processing pipelines using GCP/Azure tech stack.

- Lead the development of Retrieval-Augmented Generation (RAG) systems and multi-agent architectures.

- Establish and maintain robust LLMOps/MLOps practices for model lifecycle management.

- Develop and deploy models using PyTorch, MLFlow, and FastAPI.

- Ability to implement scalable ML infrastructure, Kubernetes, and CI/CD automation (GitHub Actions).

- Collaborate with cross-functional teams to drive cloud adoption and IT infrastructure modernization.

Requirements :

- AI/ML Tools : LangChain, LangGraph, PyTorch, MLFlow, Cloud Platforms : GCP Vertex AI, Kubernetes

- DevOps and Automation : GitHub Actions.

- Bachelor's degree in computer science, Engineering, or related field.

- 10+ years of experience in IT systems and AI/ML solution delivery.

- Proven track record in designing and deploying enterprise AI solutions.

- Strong understanding of cloud-native architectures and scalable AI infrastructure.