Key Responsibilities :

- Design and develop Agentic AI applications leveraging LLMs such as GPT, Claude, Gemini, Llama, and Mistral.

- Build AI Agents capable of reasoning, planning, tool usage, and autonomous task execution.

- Develop RAG-based solutions using vector databases and enterprise knowledge repositories.

- Implement prompt engineering, fine-tuning, and optimization techniques to improve model performance.

- Integrate AI applications with APIs, databases, enterprise systems, and cloud services.

- Build multi-agent workflows using frameworks such as CrewAI, LangGraph, AutoGen, Semantic Kernel, and LangChain.

- Develop conversational AI, virtual assistants, copilots, and knowledge management solutions.

- Monitor AI applications for accuracy, scalability, security, and governance requirements.

- Collaborate with business stakeholders to identify AI use cases and translate them into technical solutions.

Must-Have Skills :

- Python Development

- LLMs (GPT, Claude, Gemini, Llama)

- Agentic AI Frameworks (LangGraph/CrewAI/AutoGen)

- RAG Implementation

- Vector Databases

- Prompt Engineering

- API Integrations

- Cloud (Azure OpenAI/AWS Bedrock) "ANY"

- Production Deployment Experience

GenAI Application Development :

- Generative AI

- OpenAI GPT, Azure OpenAI, Gemini, Claude, Llama

- Prompt Engineering