AI/ML engineer and 2026 B.Tech Artificial Intelligence & Data Science graduate from Anna University, focused on building applied AI systems with RAG, NLP, vector search, FastAPI, PyTorch, and production-oriented ML workflows.
I build projects that connect model behavior with real software systems: repository intelligence, semantic ticket routing, API-based model inference, and data pipelines. I am comfortable reading documentation, debugging independently, writing clean code, and improving when I get stuck.
- Generative AI and Retrieval-Augmented Generation (RAG)
- NLP with embeddings, semantic search, and vector databases
- PyTorch model training and model evaluation
- FastAPI services for AI/ML inference
- Cloud deployment basics with AWS EC2 and Docker
- Clean documentation for recruiter and engineering review
Production expansion of research presented at IEEE ICIRCA 2026.
- Built around SBERT embeddings, FAISS vector search, and confidence-based semantic routing.
- Expanding into a production-oriented system with PyTorch multi-task learning, FastAPI inference, Weights & Biases tracking, and a balanced 108K+ ticket dataset.
- Predicts Category, Team, Priority, and ETA from support ticket text.
Repository: https://github.com/srinath2934/An-End-to-End-Semantic-AI-System-for-Automated-Support-Ticket-Handling
A RAG system for asking natural-language questions over GitHub repositories.
- Implements repository ingestion, source chunking, embedding generation, vector retrieval, and LLM-grounded answers.
- Uses Python, LangChain, Hugging Face / SBERT embeddings, vector search, Groq Llama, Streamlit, and Dockerized infrastructure.
- Designed to help developers understand large codebases faster with source-grounded responses.
Repository: https://github.com/srinath2934/RepoChat
Production-oriented computer vision inference project.
- Built a PyTorch object detection pipeline using YOLOv9.
- Developed FastAPI inference APIs and deployed with Docker on AWS EC2.
- Reduced inference latency from 3-5 seconds to 110ms through model-serving optimization.
Machine learning model for credit risk prediction.
- Built with Python, CatBoost, Scikit-learn, feature engineering, and SHAP explainability.
- Achieved 90% accuracy and 0.74 ROC-AUC.
AI/ML: Python, PyTorch, Scikit-learn, CatBoost, model evaluation, feature engineering, SHAP
Generative AI: LangChain, RAG, LLM APIs, prompt engineering, Hugging Face Transformers
NLP and retrieval: SBERT, embeddings, semantic search, FAISS, vector search
Backend and deployment: FastAPI, REST APIs, Docker, AWS EC2, Linux, Git, GitHub
Data: SQL, Pandas, NumPy, data cleaning, statistical analysis, Power BI, Excel
- LangChain Certification - LLM application and RAG workflow development
- Introduction to Model Context Protocol (MCP) - Anthropic
- IBM Python for Data Science, AI & Development
- IBM Python Project for Data Science
- SQL (Intermediate) - HackerRank
B.Tech - Artificial Intelligence & Data Science
JCT College of Engineering & Technology, Anna University
2022 - 2026 | CGPA: 8.0 / 10
LinkedIn: https://www.linkedin.com/in/srinath29
Email: srinath2934@gmail.com