Key Responsibilities:

- Design, develop, and deploy GenAI and Agentic AI applications.

- Build AI agents using LangGraph, AutoGen, CrewAI, or similar frameworks.

- Develop RAG-based applications, LLM solutions, and multi-agent systems.

- Translate business requirements into scalable AI solutions.

- Work with engineering and data teams to deliver production-ready solutions.

- Deploy and scale AI applications using cloud, Docker, Kubernetes, and CI/CD.

- Ensure scalability, security, reliability, and maintainability of AI solutions.

Requirements:

- 7+ years of experience in AI/ML, with strong hands-on experience in GenAI/LLM/Agentic AI.

- Strong proficiency in Python.

- Hands-on experience with RAG, LLMs, prompt engineering, embeddings, and vector databases.

- Experience with LangGraph, AutoGen, CrewAI, or similar Agentic AI frameworks.

- Experience with OpenAI, Azure OpenAI, LLaMA, Anthropic, or similar LLM platforms.

- Strong experience in developing and deploying production-grade AI applications.

- Good understanding of APIs, microservices, cloud, and distributed systems.

Domain & Cloud Experience Mandatory:

- HCLS: Healthcare / Life Sciences domain experience with Azure is mandatory.

- BFSI: Banking / Financial Services / Insurance domain experience with AWS is mandatory.

- Experience in both HCLS + BFSI with Azure + AWS will be an added advantage.

Good to Have:

- MLOps / LLMOps experience.

- Docker, Kubernetes, CI/CD.

- Kafka / RabbitMQ.

- Terraform / CloudFormation.

- Experience with AI monitoring and observability.