I design and deploy machine learning architectures that operate reliably in demanding environments. My focus bridges generative modeling, computer vision, and robust data infrastructure—taking complex requirements from initial architecture through to production scale.
I prioritize system resilience, latency optimization, and observable metrics just as highly as algorithmic performance. Leading engineering efforts means I work closely with cross-functional teams to ensure the solutions we build directly address core business challenges. Day to day, I translate ambiguous problems into structured technical roadmaps and deliver systems that drive measurable outcomes.
Experience
Chief AI Engineer, Plexor
Oct 2025 to Present
Direct the architecture and deployment of real-time object detection and vision-language models for retail surveillance.
Design fault-tolerant edge AI pipelines that operate reliably in constrained store environments.
Push the boundaries of detection precision, establishing new benchmarks for the platform's vision capabilities.
Junior Specialist, University of California, Riverside
Jul 2024 to May 2025
Developed deep learning classifiers for mosquito orientation, reaching 96% accuracy.
Built robust image processing workflows utilizing thresholding and edge detection to isolate critical biological features.
Streamlined deployment pipelines, enabling interdisciplinary teams to scale feature extraction for broader studies.
Research Engineer, University of Guelph
Jan 2023 to Jun 2023
Combined image segmentation with LLM-driven text analysis to evaluate soil properties, yielding an 18% accuracy gain.
Engineered automated tooling that cut data processing time by 40% and improved indicator precision.
Partnered directly with domain experts to fine-tune model heuristics for real-world agricultural use cases.
Computer Vision Researcher, Avawatz, UT Dallas, IIT Bombay
Mar 2022 to Jul 2022
Trained object detection algorithms for autonomous navigation using Faster R-CNN and YOLO, driving a 15% accuracy improvement.
Leveraged 3D simulations to boost segmentation precision by 20% for off-road environments.
Optimized preprocessing pipelines to reduce large dataset training times by 25%.