AI/ML Researcher & Deep Learning Engineer
I build intelligent systems across computer vision, multimodal learning, explainable AI, and generative AI — from research prototypes and scientific data pipelines to deployable AI applications.
Currently exploring AI/ML engineering and applied research opportunities involving deep learning, computer vision, GenAI, and intelligent systems.
Currently pursuing an M.Tech in Artificial Intelligence & Data Science at Alliance University, building on a B.Tech in Computer Science and Engineering with a specialization in Artificial Intelligence. My work spans explainable deep learning, computer vision, remote sensing, knowledge-augmented generation, and applied machine learning research.
I work with PyTorch, TensorFlow, Scikit-learn, OpenCV, YOLO, RT-DETR, LangChain, LangGraph, FAISS, spaCy, and Python. I also build backend systems using Java, Spring Boot, FastAPI, Flask, and REST APIs, with experience connecting machine-learning pipelines to interactive applications.
My projects focus on turning research ideas into reproducible systems: designing data pipelines, implementing and evaluating models, performing ablation and comparative studies, and exposing the resulting systems through practical interfaces and APIs.
Production ML pipelines, backend services, and scalable deployment engineering:
Peer-reviewed publications in Explainable AI, computer vision, and scientific deep learning
Computer-vision pipeline for Chandrayaan-2 OHRC imagery, covering PDS4 preprocessing, 640×640 tiling, dataset preparation, and boulder detection using multiple object-detection models.
End-to-end traffic prediction system using live traffic data, temporal and weather features, sliding-window sequences, LSTM forecasting, and an interactive web interface.
Knowledge Graph RAG system for querying ISRO mission documentation using entity extraction, graph retrieval, FAISS vector search, and local LLM inference.
Air-quality prediction pipeline using Ant Colony Optimization for feature selection, with convergence analysis and interpretable machine-learning outputs for AQI forecasting.
Customer-segmentation framework combining RFMS metrics with Fuzzy Formal Concept Analysis, stability analysis, clustering, benchmarking, and cross-domain validation.
Streamlit-based educational assistant using Google Gemini to explain concepts, generate examples, and create interactive quizzes from user-provided topics.
Multi-agent LangGraph workflow that parses job descriptions, evaluates resumes, identifies skill gaps, and generates tailored resume content.
Automated speech-to-text and NLP pipeline that converts recorded lectures and meetings into concise summaries and key points.
Browser-based 3D educational environment built with Three.js and WebGL, featuring interactive scenes, lighting, geometry, and camera controls.
Industry-verified credentials in AI systems, Java engineering, and data science
AI/ML research, Explainable AI, computer vision, engineering roles — or just hello. I'd love to hear from you.
Bangalore / Kochi, India · English (fluent), Malayalam (native)