Chicago, Illinois, United States
Senior AI Engineer and Technical Leader with 6+ years delivering production-grade GenAI and ML solutions that drive measurable business impact. Proven expertise building scalable AI platforms across finance, manufacturing, and enterprise SaaS environments.
- Leading GenAI platform development as Product Owner, architecting and deploying Guardrails/Ground Truth-as-a-Services, MCP servers and Co-Pilots across internal applications to establish enterprise-wide AI governance and evaluation frameworks - Scaled platform deployment across Azure infrastructure using Kubernetes and Argo CD pipelines, implementing secure compliance frameworks and rigorous testing protocols for enterprise-ready applications - Accelerated platform adoption through documentation creation, organizational outreach, and direct application team integration support, implementing access controls that delivered 40% reduction in onboarding time across the organization - Mentored Data Science teams to build treasury forecasting tools for monthly balance predictions, enabling compliant investment decisions
- Led $1M Manufacturing 4.0 ML initiative, developing inverse modeling algorithms for glass product quality monitoring, delivering automated tomography analysis for Gorilla® glass production lines - Built predictive ML models for glass composition optimization in consumer electronics, reducing experimental costs and accelerating time-to-market through data-driven color prediction algorithms - Deployed RAG-enhanced AI framework automating Six-Sigma workflows, reducing hypothesis validation time by 60% and earning internal innovation awards - Engineered predictive analytics platform using molecular simulation data to forecast material properties, delivering 10X faster performance compared to traditional experimental methods - Engineered proprietary Scientific software modeling glass physics for Corning's $5B Gorilla® Glass product line, improving reliability predictions and performance optimization - Developed optimization algorithms for heat treatment identification in ULE® glass manufacturing, accelerating EUV lithography alignment processes over traditional experimental methods - Built Python analytics platform extracting performance metrics from experimental data, enabling next-generation Display glass composition discovery and commercialization since 2019 Product Engineering and Customer Engagement - As a Gorilla® Glass SME, providing valuable guidance to Fortune 500 consumer electronics clients on their current and future products - Developed a proprietary production process for backlight panels in next-gen display panels leading to 30% increase in the display brightness
Launched a B2B company in biomotion analytics to reduce workplace injuries and improve workforce productivity primarily targeted towards wrist injuries. As a Co-Founder we established client relationships with Fortune 500 and Global 500 companies in food processing industry and ed the company through multiple startup accelerators, incubators and brand transition. We were able to seed money and prototyping resources valued $ 500K. As a Lead Engineer, I led team of 5 engineers to develop bio-motion analytics dashboard and hardware for wrist injury prevention, which provided ergonomics information in the plant at various levels of granularity collected using wearable IoT devices.
Thesis Advisor: Dr. Chung-Yuen Hui, Field of Theoretical and Applied Mechanics, Mechanical and Aerospace Engineering Neuro-transmission is an intricate process involving an interplay between electric impulses and chemical transfer between neurons. For my doctoral research I conducted research on theoretical and numerical modelling of biological membrane fusion under the influence of SNARE proteins in extremely specific physiological conditions. My primary focus was on synaptic vesicle fusion and docking aspect of neuro-transmission. I developed rigorous analytical and computational models to capture mechanics of cell membrane deformation and pinned down the optimal requirements for a successful neurotransmission. I used coarse-grained molecular simulations to capture the molecular details of the membrane fusion and understand the underlying steps of the fusion process. My doctoral work is published in three impactful journal articles.
Thesis Advisor: Dr. Sovan L Das, Department of Mechanical Engineering The cell shape remodelling is an inevitable process carried out by proteins, with applications ranging from endoytosis, exocytosis, tether formations etc. For my Master's research, I worked on the interplay between the membrane curvature and peripheral proteins by combining the thermodynamics of protein distribution and continuum mechanics model of biological membrane deformation. Using this hybrid model, I deduced the membrane shape details upon the variation of different physiological parameters. My Master's research work is published in one journal article.