I'm a Data Scientist at HiLabs, specializing in Agentic AI, Generative AI, and applied Machine Learning. I graduated from BITS Pilani with a B.E. (Hons.) in Mechanical Engineering and a minor in Data Science, where I developed a strong foundation in machine learning, deep learning, statistics, and optimization.
My experience spans healthcare technology, financial services, and AI-driven automation. At HiLabs, I build production-grade AI systems, including a multi-agent QA automation platform that reduced test-suite turnaround time from 4–5 days to just 5–6 hours. I've also optimized LLM-powered data extraction pipelines, reducing processing time from 13 seconds to 3 seconds and cost per document from $1.40 to $0.80.
Previously, as a Data Science Intern at Franklin Templeton, I developed machine learning models for fixed-income analytics, built portfolio optimization frameworks using Monte Carlo simulations and Black-Litterman-based return estimates, and automated CLO primary-market data collection, delivering approximately $30,000 in annual savings. At Consat Orahi, I built a Retrieval-Augmented Generation (RAG) assistant and a multi-agent text-to-SQL workflow to automate information retrieval and analytics.
My technical interests include agentic AI systems, LLM reasoning and evaluation, RAG, multi-agent architectures, model optimization, and applied ML. I also explored reasoning fidelity in quantized small language models, benchmarking quantization and inference-time techniques and fine-tuning models using GRPO and LoRA.
I enjoy solving complex, real-world problems by combining rigorous data science with practical software engineering. My toolkit includes Python, SQL, PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, FastAPI, Docker, and AWS.
I'm particularly interested in opportunities across Data Science, Machine Learning Engineering, Applied AI, and Generative AI, where I can build scalable, reliable AI products and translate research and experimentation into measurable business impact.
