Work & Projects
I work on production systems across multiple domains, from scaling backend infrastructure to deploying AI models in regulated environments. My focus is solving real-world problems with practical engineering—grounded in constraints and measured feedback.
This page is a high-level view of the kinds of systems I've worked on and the engineering approaches I rely on for reliability, observability, cost control, and safe iteration.
Details vary by context, so descriptions stay intentionally general.
Types of Systems I've Worked On
Production AI Systems
LLM deployments, RAG pipelines, and inference optimization. Focus areas include evaluation, observability, cost controls, and latency budgets.
MLOps Infrastructure
Model versioning, experiment tracking, and automated retraining pipelines—plus CI/CD workflows for ML systems.
Backend Systems
High-throughput APIs, distributed task processing, and database optimization—designed for reliability and performance under real-world load.
Data Pipelines
ETL workflows, real-time processing, and batch jobs with monitoring, alerting, and data quality checks.
Problem-Solving Approach
I start by understanding the business problem before writing code. Many AI projects fail when teams build solutions without clarifying the underlying requirement.
- Clarify constraints: Budget, latency, accuracy, and compliance requirements upfront
- Start simple: Build a baseline before adopting complex architectures
- Measure early: Add instrumentation and logging from day one
- Plan for failure: Systems degrade gracefully and alerts trigger before users notice
Collaboration
I use this site to share practical notes, patterns, and references from real engineering work—focused on reliability, clarity, and safe changes in production environments.
If you have feedback on an article, want to suggest a topic, or have a general question about the content, you can reach me via the contact page.
I don't share confidential details or proprietary implementation specifics.