• Designed and implemented data storage and retrieval software for embedded wireless IoT devices, enabling reliable communication across large device fleets.

• Developed continuous data ingestion integrations from the Blues Wireless platform into Snowflake, heavily utilizing AWS services including S3, SQS, and IAM roles.

• Architected and deployed CI/CD pipelines using GitHub Actions to automate and streamline application deployments.

• Engineered automated integration testing frameworks against staging environments to validate performance and reliability directly on live embedded devices.

• Independently managed client relationships, translating technical requirements into custom software solutions that met client-specific needs.

• Led technical hiring process: sourced, interviewed, and onboarded software engineers, contributing to team growth.

• Developed backend systems in Go for Comcast’s customer-facing ISP speed test platform, integrating multiple internal services into a cohesive data pipeline.

• Designed and implemented a streaming-based data loading architecture, replacing a bulk-load approach and significantly improving system throughput and efficiency.

• Integrated CouchDB (NoSQL) for scalable, low-latency data persistence supporting high-volume test metrics.

• Wrote and maintained comprehensive test suites to ensure reliability and correctness of backend services.

• Contributed to an in-house incident management and alerting platform that monitored production services via API health checks and triggered automated incident response workflows.

• Implemented integrations with external APIs including Slack and Zoom to automatically create dedicated incident channels and meetings upon detection of service failures.

• Developed email and SMS notification pipelines to alert on-call engineers of critical system events.

• Leveraged AWS cloud services to support platform infrastructure and event-driven workflows.

• Wrote extensive automated test coverage and resolved bugs to improve platform stability and reliability.

• Built backend functionality in Go for an internal CLI tool enabling researchers to rapidly provision high-performance compute environments in AWS, purpose-built for genomics and bioinformatics workloads.

• Implemented data transfer pipelines to efficiently ingest and manage large-scale biological datasets (DNA/RNA) into cloud-based compute environments.

• Contributed to a productivity-focused developer tool that abstracted complex AWS infrastructure, reducing friction for scientific computing teams.

• Co-founded an AI-driven industrial monitoring startup, leading a team of two engineers and providing technical guidance to the CEO.

• Architected and built a full-stack intelligent video streaming platform using Go, React/TypeScript, Python, and C/C++.

• Built and maintained scalable machine learning model training pipelines leveraging Google Cloud infrastructure.

• Designed and trained convolutional neural network (CNN) image recognition models and deployed them for real-time inference on live video streams from ruggedized industrial cameras.

• Delivered ML-powered monitoring solutions for high-stakes industrial environments including mining operations and chemical processing plants, enabling automated visual inspection and real-time condition alerts.

• Developed an ML model that analyzed the chemical composition of extracted rock samples from live video, replacing a manual lab-testing process and improving operational efficiency for a mining client.

• Oversaw embedded systems programming, system administration, and network configuration for edge-deployed camera hardware.