About
Career Context
I'm a Senior Software Engineer with over a decade of experience working across quality engineering, automation, DevOps, and platform reliability. I started my career focused on how systems fail, not just how they succeed, which shaped how I approach software engineering today.
Over time, my work expanded from test automation into system-level validation, CI/CD pipelines, and cloud-based platforms. This background gave me a deep appreciation for reliability, observability, and the importance of understanding failure modes in distributed systems.
How I Think About AI
As AI systems became part of everyday engineering workflows, I noticed a gap: many AI solutions focused on capability, but very few focused on reliability, evaluation, or operational safety. This portfolio exists to explore that gap.
What This Portfolio Represents
The projects here are not demos. They are applied AI systems designed for real engineering environments with strict evaluation, clear refusal paths, security controls, and human-in-the-loop decision-making. I believe AI should support engineers, not replace judgment, and that production AI systems should be explainable, observable, and safe by default.
This portfolio reflects how I think about AI: as a system that must earn trust through evidence, not assumptions.
- Reliability-first AI behavior
- Evidence and citations where applicable
- Strict refusal when uncertainty is high
- Guardrails (security + privacy)
- Automated evaluation and regression testing
- Human-in-the-loop decision making