CGPA: 3.20 / 4.00

Topic: Automatic Perception Generation from Doctor-Patient Conversations.

• Partnered with the Business Intelligence (BI) team to align analytics, dashboards, and machine learning outputs with business KPIs and stakeholder requirements.
• Translated business questions into analytical problems, defining metrics, features, and success criteria in collaboration with BI and product teams.
• Designed, trained, and deployed machine learning and deep learning models in production environments to support data-driven products and decision-making.
• Built and maintained end-to-end MLOps pipelines including data ingestion, model training, validation, versioning, deployment, and monitoring.
• Deployed scalable ML solutions on cloud platforms (AWS), leveraging services like S3, EC2, Lambda, SageMaker.
• Designed and implemented Agentic AI systems, enabling autonomous task execution, tool calling, and multi-step reasoning using frameworks such as LangChain or similar.
• Developed RESTful APIs and microservices to serve ML and LLM models at scale.
• Mentored and guided data science interns, providing hands-on support in Python, SQL, machine learning, and data analysis.

• Developed NetAgent, an agentic orchestration system that integrates multiple AI tools, performs real-time log analysis, and enhances operational intelligence.
• Developed RESTful APIs for LLM and Agentic AI applications using FastAPI, ensuring high performance and scalability through Docker containerization.
• Designed and implemented relational data models using SQLAlchemy ORM and integrated Neo4j graph databases for knowledge graph-based reasoning.
• Built and optimized RAG and GraphRAG pipelines leveraging vector databases and graph databases, improving LLM response accuracy.
• Developed agentic AI workflows using LangChain, CrewAI, and LLM-based task automation, enabling dynamic multi-agent collaboration.