I am a B.Tech student specializing in AI & ML who enjoys taking ideas from a model or algorithm and turning them into complete software systems.
My work sits at the intersection of Generative AI, Retrieval-Augmented Generation, machine learning, backend engineering, and data-driven applications. I focus on systems that are structured for evaluation, deployment, observability, and reliability.
- Generative AI & RAG — hybrid retrieval, GraphRAG, embeddings, reranking, evaluation, and LLM applications
- AI Agents — LangGraph workflows, tool calling, planning, memory, routing, and human-in-the-loop execution
- ML Engineering — NLP, forecasting, classification, feature engineering, model evaluation, and deployment
- Backend Engineering — FastAPI, REST APIs, PostgreSQL, Neo4j, authentication, caching, and service design
- MLOps & DevOps — Docker, CI/CD, reproducible benchmarks, monitoring, and production-oriented workflows
| Focus Area | What I am building |
|---|---|
| RAG Systems | Hybrid vector + BM25 + graph retrieval with reranking and evaluation |
| Agentic AI | Stateful workflows with planning, tools, routing, and review loops |
| AI Engineering | FastAPI services around ML and LLM systems |
| System Reliability | Fallbacks, retries, caching, evaluation, and deterministic CI |
Production-oriented RAG architecture combining semantic, lexical, and graph retrieval.
PDF → Parsing → Semantic Chunking → Embeddings → PostgreSQL/pgvector + Neo4j → Query Classification → Vector/BM25/Graph Retrieval → RRF → Cross-Encoder Reranking → LLM Generation
- Hybrid vector + BM25 + Neo4j graph retrieval
- Reciprocal Rank Fusion and cross-encoder reranking
- Query classification and retrieval-strategy routing
- RAGAS evaluation with deterministic fallback
- Authentication, rate limiting, caching, memory, and analytics
- Dockerized services and GitHub Actions CI/CD
→ Repository: Hybrid-GraphRAG-System
Agentic workflow for turning high-level goals into executable tasks.
- LangGraph state-machine architecture
- Planning → execution → review → routing
- Tool registry, session state, retries, and human input
→ Repository: -Task-Planner-Agent
Spatio-temporal graph neural network for traffic forecasting across road sensors.
- PyTorch STGCN architecture
- Graph convolution + temporal convolution
- FastAPI backend + Streamlit interface
→ Repository: stgcn-traffic-forecasting
End-to-end machine learning application with deployment and CI/CD.
- Scikit-learn pipeline
- FastAPI inference service
- Streamlit frontend
- JWT authentication and Docker Compose
→ Repository: churn-prediction-ml-api
Document question-answering system using retrieval, reranking, and evaluation.
- PDF ingestion and chunking
- Vector + BM25 retrieval
- RRF, cross-encoder reranking, memory, and RAGAS
→ Repository: Rag
I contribute to open-source projects by identifying concrete bugs, improving dataset metadata, adding regression coverage, and extending developer tooling.
Pull Request #2472 · Open
Added a YAML-to-Markdown converter supporting .yaml and .yml files, including nested mappings, lists, tables, multiple YAML documents, anchors/aliases, multiline strings, and edge cases. Added converter registration and comprehensive tests.
Pull Request #14 · Merged
Fixed a FastAPI route-ordering bug where DELETE /api/executions/all was being captured by the parameterized DELETE /api/executions/{execution_id} route. Added regression tests covering both bulk and single-execution deletion.
Pull Request #8 · Merged
Improved secret-leak detection so exact-case and case-insensitive matches are distinguished, with regression tests for both behaviors.
Contributed multiple fixes to awesomedata/apd-core, including:
- Updated the MIT Heart Rate Time Series dataset link to the PhysioNet archive. PR #386 — merged.
- Updated USDA Nutrient Database metadata to reflect FoodData Central. PR #387 — merged.
- Updated the CIMA histological microscopy dataset link to an accessible Kaggle mirror. PR #388 — merged.
- Fixed the Cell Image Library homepage URL and metadata. PR #389 — merged.
→ View Awesome Data contributions
Problem → Data / Documents → Retrieval or ML Pipeline → Model / LLM
→ Evaluation → API / Product → Reliability → Deployment / CI/CD
Retrieval quality · evaluation · latency · failure handling · security · reproducibility · deployment
| Area | Technologies / Concepts |
|---|---|
| Generative AI | LLMs, prompt engineering, RAG, GraphRAG, tool calling |
| Retrieval | pgvector, BM25, graph retrieval, RRF, reranking |
| Agents | LangChain, LangGraph, stateful workflows, memory |
| NLP | TF-IDF, transformers, BERT, classification, semantic similarity |
| Deep Learning | PyTorch, TensorFlow, GNNs, STGCN, transfer learning |
| Machine Learning | XGBoost, Random Forest, Logistic Regression, SVM, clustering |
| MLOps | MLflow, Docker, GitHub Actions, evaluation pipelines |
| Backend | FastAPI, REST APIs, PostgreSQL, Neo4j, authentication |
- Data structures and algorithms
- SQL and data modeling
- System design for AI applications
- Prompt engineering and LLM evaluation
- Experimentation and model analysis
- Writing maintainable, testable Python
I am interested in opportunities and collaborations around AI/ML engineering, Generative AI, RAG, intelligent agents, and backend systems.