AI/ML · Backend Systems · Distributed Architecture
Designing and building resilient backend systems and AI/ML architectures-from high-scale distributed services and storage engines to neural networks and inference pipelines built from first principles.
I write about DISTRIBUTED SYSTEMS, BACKENDS & AI/ML - mostly questions I can't stop thinking about.
About
Building software is less about simply writing code and more about understanding problems deeply enough to build the right thing.
Rather than treating frameworks or models as black boxes, the focus is on what makes them work-the storage engines and concurrency models powering distributed backends, the mathematical foundations of gradient descent and transformers, and the engineering trade-offs that shape production scalability.
Understanding why something works is often far more valuable than simply knowing how to call an API.
That first-principles mindset proves especially valuable when problems are ambiguous-whether diagnosing subtle distributed failures or optimizing custom ML training and inference pipelines.
How I work
Start with the problem, not just the ticket.
Understand the problem, map the constraints, choose the simplest approach that works, ship it, and stay close enough to the system to learn from what happens in production.
The interesting problems begin where there is no obvious answer.
Designing distributed architectures. Identifying subtle failure modes. Eliminating latency bottlenecks and race conditions. Asking:
“What happens when this platform scales to millions of requests or gigabytes of model state?”
Then engineering the answer.
In AI and machine learning, genuine intuition comes from stepping outside high-level libraries. Implementing autograd engines, training loops, and transformer inference engines from scratch builds mechanical sympathy for how gradients flow, how memory is managed, and where computational bottlenecks hide.
Models don't live in a vacuum-they rely on resilient production backends around them. Bridging AI/ML with backend engineering means building the real-world infrastructure: low-latency inference APIs, vector retrieval pipelines, caching strategies, and reliable distributed state that hold up under production load.
The best way to understand complex systems is often from the ground up: build it, stress-test it, break it, and diagnose why it failed.
Every failure becomes another piece of the system now understood.
Approach
Learn. Build. Break. Understand. Build better.
Whether you're scaling a distributed backend, deploying AI/ML systems to production, designing data pipelines, or tackling deep performance bottlenecks, feel free to reach out.