Operational forecasting with machine learning
Designing end-to-end ML pipelines that predict time-critical operational events at scale, and the MLOps practices that keep them reliable in production.
Sharing what I’ve learned building ML and distributed systems.
I care about technical storytelling, mentorship, and making complex systems understandable. These are the topics I most enjoy talking through — in design reviews, mentoring sessions, and conversations with engineering teams.
Topics I speak to
Event-driven and serverless architecture on AWS
Patterns for decoupling systems with EventBridge, SQS, Lambda, DynamoDB, and Step Functions — and the trade-offs that come with moving away from tightly coupled designs.
Scaling and reliability under real load
What it takes to take dropped messages to zero, cut cost and latency by orders of magnitude, and keep distributed systems dependable as they grow.
Mentorship & leadership
Technical leadership and code review at scale
Leading design sessions, running effective code reviews, and mentoring engineers toward maintainable, DRY, well-tested code.