I’m a platform-focused software engineer with a strong background in distributed systems, cloud infrastructure, and data-intensive applications. My work centers on designing and building scalable, reliable systems that support high-volume data processing, observability, and operational insight.
I’ve led initiatives that improve system stability, reduce operational overhead, and introduce consistent patterns for configuration and governance. I’m particularly interested in creating composable, extensible solutions — whether through infrastructure automation, policy enforcement systems, or event-driven architectures — that allow teams to move faster without sacrificing control or reliability.
Currently, I focus on generative AI security, evaluating emerging risks and helping organizations navigate the evolving landscape of AI adoption. This includes assessing how generative AI systems are integrated into real-world workflows, identifying potential vulnerabilities, and contributing to the development of safer, more reliable AI-driven solutions across industries.
My experience spans backend services, cloud-native infrastructure, and data pipelines, with a focus on pragmatic engineering: choosing the right tools, understanding trade-offs, and delivering systems that are maintainable in the real world. Having worked across multiple domains, I’m able to quickly understand business needs and translate them into effective technical solutions.
I’m particularly interested in the intersection of systems design and emerging technologies like machine learning and AI, and how they are reshaping the role of engineers. Long term, I aim to operate at the architectural level — guiding technology decisions, building proofs of concept, and shaping systems that align with both technical and business goals.
Outside of work, I’m interested in mathematics, creative writing, game development, and building tools that help me think and organize better — this site and the notebox behind it being one of those projects.