At Honeywell Technologies, I developed an NLP regulatory risk-scoring engine that used named entity recognition, classification models, and LLM-assisted rule validation to quantify compliance gaps in APQR reports. I also created a time-series monitoring framework that turned regulatory data into dashboard-ready insights.
I automated more than 20 regression workflows at Honeywell Technologies, reducing testing time by about 10 hours per cycle and improving reliability KPIs by 10%. Earlier, as a Software Engineer Intern at TATA Motors Ltd, I developed a data transformation pipeline for vehicle test-bed data that reduced operational costs by about £6,000 per month.
My projects include an A&E analytics platform for staffing optimisation and breach prediction, and a power plant optimisation model that identified compliance strategies with more than 40% emission reductions. I work with Python, SQL, machine learning, and optimisation modelling.
