Develop a full-stack application using TypeScript, NextJS, and React for image and video management, allowing customers to apply custom computer vision models for classification, object detection, and segmentation tasksEngineer scalable image and video processing pipelines in Go, handling concurrent daily uploads with efficient tagging and metadata extraction, enabling real-time sorting and filtering of media assetsArchitect a computer vision model training platform using FastAPI and PyTorch, deployed on Kubernetes for efficient scaling which reduced client onboarding time from 2 weeks to 1 day by automating model fine-tuning processes

Developed an interactive 3D map application using TypeScript, NextJS, and React to visualize methane emissions data from 100+ monthly drone flights, reducing data rendering time from 10 seconds to 2 secondsRefactored REST APIs to gRPC endpoints in Go for geospatial data ingestion, reducing average latency from 200ms to 50ms per request and decreasing error rates from 4.9% to 0.1%Engineered efficient geospatial data processing pipelines using Prefect, GeoPandas, and Python, reducing processing time from 4 hours to 1 hours

Architected end-to-end machine learning workflows using PyTorch, scikit-learn, and Python for crop yield prediction, reducing model training time from 48 hours to 24 hours and improving prediction accuracy from 82% to 91% on a dataset of 10 million crop samplesDeveloped and optimized big genetic data processing pipelines using Prefect, PySpark, and Python, increasing data throughput from 20GB/hour to 300GB/hourImplemented an IoT ingestion engine using Gin and Go to process real-time data from 1,000 field sensors, enabling near real-time crop health monitoring

Migrated ETL pipelines from Luigi to Prefect in Python, automating the export of 100GB of daily geospatial data updates, reducing processing time from 4 hours to 1.5 hours and decreasing manual interventions from 15 to 5 per weekDeveloped a web application using TypeScript, NextJS, and React for visualizing land parcel boundary availability and recency, handling 10 million parcels across the United States and CanadaOptimized PostgreSQL geospatial database queries by implementing custom indexes and table partitioning, improving system throughput from 200 to 500 queries per minute

Developed a frontend using TypeScript and React for visualizing archaeological spatial data, reducing page load times from 5 seconds to 1 seconds using caching techniquesBuilt a Django REST API in Python to serve geospatial data, handling over 1,000 requests per minute and enabling real-time updates for 10+ field crews simultaneouslyImplemented efficient geospatial data processing workflows using GeoPandas, Apache Airflow, and Python, reducing processing time of archaeological datasets from 24 hours to 6 hours