AI/ML engineering student with hands-on experience in fine-tuning LLMs, building multi-agent reasoning systems, and deploying scalable data pipelines. Skilled in Python, PyTorch, TensorFlow, and LangGraph.
Fine-tuned LLMs for domain-specific tasks, improving contextual accuracy by 15%., Deployed Crawl4AI pipelines, reducing preprocessing time by 30%., Designed multi-node reasoning flows with LangGraph, enabling structured logic chains for complex queries.
Developed domain-adaptive LLM solutions through full-cycle experimentation with advanced tooling and data workflows.
Fine-tuned LLMs for domain-specific tasks, improving contextual accuracy by 15%.
Deployed Crawl4AI pipelines, reducing preprocessing time by 30%.
Designed multi-node reasoning flows with LangGraph, enabling structured logic chains for complex queries.
Dynamic Spatiotemporal Crime Analysis Built predictive models on Toronto crime dataset using DBSCAN clustering and LSTM/TCN architectures, achieving accurate hotspot forecasting for public safety planning.Lecture Notes Generator Automated structured note creation from recordings using Transformers, Hugging Face, CLIP, and Groq, improving study efficiency by 40% in pilot tests.Agentic AI Web Research Agent Developed autonomous multi‑agent system with LangGraph and NLP, enabling scalable web research and summarization with measurable accuracy gains.PEStivity: Created an arcade game site using react and tailwind along with HTML and JS.Blackjack: Developed a multiplayer Blackjack game using Python Socket Programming.Adaptive project management using Retrieval Augmented Generation.
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