About
Highly accomplished AI/ML and Full-Stack Engineer with a strong background in developing scalable, data-driven solutions and leading innovative projects. Proven expertise in machine learning infrastructure, full-stack development, and optimizing complex systems across diverse industries, including manufacturing, distributed systems, and computer vision. Adept at leveraging advanced technologies to drive efficiency and build impactful products.
Work
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Summary
Co-founded a startup during Antler NYC9 residency, focusing on AI-driven workflow automation for manufacturing.
Highlights
Selected for Antler NYC9 residency, leading the exploration and development of AI-driven workflow automation solutions for manufacturing, focusing on smart search and operational streamlining.
Architected and developed AI and OCR solutions to extract critical insights from blueprints and unstructured documents, significantly accelerating quoting workflows for manufacturing clients.
Spearheaded full-stack development initiatives, utilizing Python, TypeScript, React, FastAPI, AWS, Supabase, and PostgreSQL to build robust and scalable applications.
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Summary
Engineered and optimized distributed systems for Redshift, enhancing efficiency and availability.
Highlights
Engineered a machine learning-based automated regression system, eliminating hundreds of hours of manual bug/feature ticket assignment weekly by autonomously tracking ticket lifecycles.
Led the architectural design and infrastructure development for a critical service, leveraging Python, TypeScript, and a suite of AWS services (Lambda, Redshift, DynamoDB, S3, SQS, SNS, CloudWatch).
Automated the cutting, de-duplication, and ownership assignment of customer tickets across over 20 AWS production regions, enhancing operational efficiency.
Optimized Redshift's concurrency scaling mechanism, achieving significant reductions in query response times and boosting system availability across AWS regions.
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Summary
Contributed to machine learning infrastructure and data pipeline development for computer vision applications.
Highlights
Developed robust machine learning infrastructure and ETL pipelines to facilitate training of deep learning models for advanced image and video-based face recognition, authentication, and hand tracking.
Created comprehensive data pipelines for processing, annotating, and generating high-quality egocentric datasets, crucial for advancing XR hand tracking capabilities.
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Summary
Interned in AI/ML, implementing and optimizing neural network architectures.
Highlights
Implemented and optimized variations of Region-based CNN and GAN neural network architectures for enhanced face recognition and object detection applications, utilizing PyTorch and TensorFlow.
Contributed to research on optimizing Deep Residual Networks training, focusing on flexpoint and dynamic fixed-point numerical formats for significant hardware acceleration.
Skills
Programming Languages
Python, TypeScript, JavaScript, SQL.
Tools & Platforms
AWS, OpenAI, Anthropic, Git, Docker, PostgreSQL, MongoDB, Supabase, Vercel.
Frameworks & Libraries
FastAPI, Pydantic, React, React Native, Next.js, Express, PyTorch, Pandas.
Machine Learning & AI
Deep Learning, Computer Vision, Natural Language Processing (NLP), Image Restoration, Neural Networks, CNNs, GANs, OCR, Statistical Learning, Recommender Systems.
System Design & Architecture
Distributed Systems, ETL Pipelines, Cloud Infrastructure, Concurrency Scaling.