Software Engineer | ML/Backend/AI | Product-driven
I turn complex technical challenges into scalable solutions that drive measurable business value. Whether it’s leading a $50k enterprise pilot at 16, optimizing a 9-figure-scale ML platform @Intuit, or building AI-powered health tech apps, my focus is on impact, speed, and precision.
I think of myself as a venture capitalist of my time, money, and impact -- seeking projects that multiply value for users, teams, and organizations.
- Optimized ML model inference from 7 hours → 36 minutes (92% faster) and cost from $600 → $89 (85% cheaper), enabling $400k+ annual savings.
- Built large-scale data pipelines with Apache Spark (via BigQuery) and Apache Beam on GCP Dataflow, processing millions of records daily.
- Containerized Spark/Beam jobs with Docker and deployed via Dataflow Flex Templates, ensuring reproducible, cloud-native workflows.
- Scaled Data Science workloads to 10x more models without increasing infrastructure cost/load.
- Designed batch processing, fixed cost vs ammortized, and parallel I/O optimizations now referenced in Google Cloud’s ML inference offerings.
- Applied systems-level thinking for production ML at enterprise scale, balancing latency, throughput, and cost.
- Working in an applied research and R&D environment focused on AI-first applications, LLMs, and GenAI for financial technology.
- At 16, led a $50k enterprise pilot for a $1B+ pharmaceutical company.
- Developed predictive and ML-powered SaaS platforms for student lifecycle management and healthcare.
- Delivered full-stack solutions with Python, React, Node.js, AWS, and GCP, integrating ML models for actionable insights.
- Built an AI-powered iOS app gamifying at-home behavior logging for parents and BCBAs.
- Achieved 92% daily engagement with reports automatically delivered to professionals, cutting session prep in half.
- Data Engineering & ML Pipelines: Apache Spark, Apache Beam, GCP Dataflow, Airflow, batch & streaming systems
- Software Engineering: Python, Java, TypeScript, React, Node.js, Django (exposure)
- Cloud & Infrastructure: GCP (BigQuery, Dataflow, Vertex AI), AWS (EC2, S3), Docker, CI/CD pipelines, Kubernetes/OpenShift concepts
- Product & Impact: Rapid prototyping, scalable enterprise software, cost/performance optimization
- 92% faster ML scoring, 85% cost reduction → $400k annual savings
- 10x model onboarding capacity without additional infrastructure
- 92% daily engagement in consumer health tech apps
- Glow (AI iOS app): Parents log behavior in 8 seconds; BCBAs get automated reports.
- 1700ventures.com: Building and iterating on ventures solving “hair-on-fire” problems.
- Enterprise SaaS: Student lifecycle management, predictive scheduling, secure cloud solutions.
- If you’re building ML platforms, scalable SaaS, or AI-driven products.
- If you want to discuss distributed data processing, Spark/Beam pipelines, or cloud-native optimization.
- I value direct collaboration -- Connect on LinkedIn or send an email.