Systems-focused engineer with a deep interest in distributed systems, OS internals, networking, and how AI can make infrastructure more observable, resilient, and autonomous.
personProfile
I'm a systems-focused engineer who loves understanding how things work under the hood — from operating systems and networks to distributed coordination and failure modes. I enjoy tracing hard failures across the stack, reasoning about concurrency and consistency, and turning flaky, manual operations into reliable, observable systems.
Recently, I've been exploring how AI augments systems work — from intelligent debugging and anomaly detection to ML-serving infrastructure and AI-assisted reliability. My goal is to build production-grade infrastructure that is both deeply principled in systems and augmented by intelligence.
Currently a Solutions Engineer at guardsix (formerly Logpoint), I work close to production — debugging distributed enterprise systems across disk, network, database, and service layers in Java and Python environments.
Consistency, coordination, replication, and failure handling in real-world production systems.
memorySystems & Infra
OS internals, networking, observability, and building reliable low-level components in Go / Rust / C++.
neurologyAI for Systems
Exploring ML-serving infra, anomaly detection, intelligent debugging, and AI-augmented reliability — e.g. partial-convolution inpainting with PyTorch.
neurologyWhy systems + AI
Systems give you correctness, performance, and reliability guarantees — AI gives you leverage. I'm interested in where they meet: ML-serving infrastructure that respects systems constraints, anomaly detection that actually reduces MTTR, and tooling that helps engineers reason faster without hiding the fundamentals.
Current explorations: Go/Rust systems programming, PyTorch for applied AI (see my partial-convolution inpainting project), and production debugging that informs better observability.