From research
to production.

Built a semantic photo search system using CLIP and BLIP for natural language image queries. Enables users to search their photo library with descriptions like "sunset at the beach" or "my dog playing." Implemented zero-shot classification and cross-modal retrieval with FAISS for efficient similarity search.

Why it's impressive: Bridges vision and language modalities using state-of-the-art transformers. Handles semantic understanding, not just keyword matching. Production-ready with sub-200ms query latency on large image collections.

Designed a routing system that dynamically selects between GPT-4, Claude, and Llama based on query complexity and cost constraints. Uses lightweight classifiers to predict optimal model choice, reducing inference costs by 40% while maintaining 95%+ quality.

Trade-offs considered: Balance between cost savings and response quality. Implemented fallback mechanisms for edge cases. Optimized routing latency to stay below 50ms overhead.

Built an autonomous agent that extracts, classifies, and stores information from unstructured text using Google Gemini API. Automatically processes documents, identifies key entities, and organizes data in Google Cloud Storage with intelligent tagging and retrieval mechanisms.

System design: End-to-end pipeline with text preprocessing, LLM-based classification, structured data extraction, and cloud storage integration. Deployed on Render with automated workflows.

AI-driven agricultural platform that recommends optimal crops, fertilizer strategies, and detects plant diseases using ML and deep learning. Built with Flask, PyTorch, and computer vision models. Processes soil data, weather patterns, and crop images to provide actionable insights.

Real-world impact: Helps farmers make data-driven decisions about crop selection and disease management. Combines traditional ML (for tabular data) with CNNs (for image classification).

AI-powered medical assistant using Retrieval-Augmented Generation (RAG) and Chainlit. Retrieves relevant medical information from knowledge base and generates contextual responses. Built with vector databases for semantic search and LLMs for natural language understanding.

Technical approach: Combines embedding models for semantic retrieval with LLMs for response generation. Ensures factual accuracy by grounding responses in retrieved medical documents.

Published research on extractive text summarization techniques. Investigated graph-based methods, sentence ranking algorithms, and semantic similarity measures for automatic summary generation. Compared approaches on benchmark datasets and proposed optimizations.

Research contribution: Demonstrated ability to conduct rigorous research, implement experiments, and communicate findings. Shows depth in NLP fundamentals and academic writing.