I am a Machine Learning and Software Engineer specializing in building scalable, production-ready AI systems. My work sits at the intersection of machine learning, DevOps, and cloud infrastructure, where I design automated ML pipelines, containerized services, and CI/CD workflows to bring models from experimentation to reliable production.
I have hands-on experience with Python, PyTorch, TensorFlow, Docker, Kubernetes, and AWS, and have built ML-powered SaaS platforms involving GPU acceleration, asynchronous job processing, and real-time monitoring. I enjoy collaborating closely with data scientists and engineers to embed MLOps best practices that improve reproducibility, observability, and system reliability.
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Research and data system to predict, monitor and analyze TTP hyperthyroidism during and prior to its onset. Used deep learning and real-time data prediction trajectory for health and services.
Built deeplearning pnpm-powered monorepo aggregating deals from Amazon, eBay, and Shopify.
Developed with NestJS, Next.js, PostgreSQL, Redis, and Meilisearch for scalable search.
Implemented ML models for price trend prediction and optimal buying windows.
Designed weighted scoring algorithm to rank offers by price, shipping, and seller rating.
Applied collaborative filtering for personalized recommendations across marketplaces.
Achieved sub-100ms search over millions of SKUs with TypeScript full-stack orchestration.
Built ML SaaS for video frame interpolation using RIFE with GPU acceleration.
Designed scalable architecture with async jobs, task queues, and real-time tracking.
Developed web interface for video upload, status monitoring, and output download.
Added AI-assisted prompt controls for guided video enhancement.
Delivered end-to-end system design across frontend, backend, GPU, and DevOps.
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