Key Responsibilities

Agentic AI System Design & Development

• Design and develop autonomous, goal-driven AI agents capable of planning, reasoning, and task execution
• Implement agentic frameworks such as LangGraph, CrewAI, AutoGPT, and AgentOps
• Build scalable multi-agent systems for enterprise healthcare use cases

LLM Engineering & Optimization

• Fine-tune and prompt-engineer LLMs such as GPT, LLaMA, Mistral, and other foundation models
• Develop domain-specific AI solutions using prompt optimization and model adaptation techniques
• Evaluate and improve model performance for reliability and accuracy

RAG & Vector Database Implementation

• Build Retrieval-Augmented Generation (RAG) pipelines for contextual AI responses
• Work with vector databases such as FAISS, Pinecone, and Weaviate
• Ensure accurate grounding of AI outputs using enterprise knowledge sources

ML Frameworks & Model Development

• Develop machine learning models using Python, TensorFlow, PyTorch, and HuggingFace
• Support training, evaluation, and deployment of AI models
• Iterate rapidly on prototypes and scale them into production systems

API & System Integration

• Build and integrate RESTful APIs and microservices for AI solutions
• Work with orchestration tools such as FastAPI and Airflow
• Ensure seamless integration of AI systems into enterprise platforms

Cloud & Production Deployment

• Deploy AI solutions on cloud platforms such as AWS, GCP, or Azure
• Ensure scalability, reliability, and performance of production AI systems
• Monitor system behavior and optimize performance

Healthcare Domain Application

• Work on healthcare insurance (payer-side) use cases and workflows
• Translate domain requirements into AI-driven solutions
• Collaborate with domain experts to ensure compliance and relevance

Collaboration & Stakeholder Engagement

• Work closely with cross-functional teams to identify AI opportunities
• Communicate technical insights to business stakeholders
• Support continuous improvement of AI products and systems