I work at the intersection of machine learning, AI systems, and applied research, with a focus on building models and systems that operate under real-world constraints.
My work is driven by the idea that model performance alone is insufficient. Decisions around data readiness, evaluation criteria, system architecture, and deployment trade-offs matter as much as the model itself.
How I work
Systems-first thinking
I approach machine learning as a systems problem, where data, models, infrastructure, and operational constraints are tightly coupled.
Robustness over benchmarks
I prioritize robustness, generalization, and failure modes over leaderboard performance.
Ownership and lifecycle
I emphasize clear ownership across the full ML lifecycle, from data readiness to deployment and long-term maintenance.
Pragmatic trade-offs
I work explicitly with trade-offs between accuracy, latency, cost, and maintainability.
Background
My background spans research-oriented development, hands-on engineering, and technical leadership.
I have worked end-to-end on applied machine learning systems, from data preparation and experimental validation to deployment and operation in production environments, including resource-constrained settings.
This experience combines academic research training with practical system design and delivery under organizational and operational constraints.
Scope
What I focus on
Applied machine learning and AI systems intended to be deployed, owned, and evolved over time.
What I avoid
Prototypes detached from production realities or solutions that cannot be sustained beyond initial experimentation.