I'm an AI engineer who enjoys the space between research and product — turning promising models into systems that are reliable, fast, and genuinely useful for the people who depend on them.
Most of my work sits at the intersection of intelligent systems and careful engineering: designing architecture that scales, sweating the details other people skip, and staying curious about how software gets built next.
Designing reliable automation workflows and building production-grade systems with a focus on scalability and maintainability.
Maintained data accuracy by validating datasets, identifying inconsistencies, and improving the reliability of business reporting.
Improved deployment reliability and system observability across continuous integration and continuous delivery pipelines and application hosting environments.
Supported daily office operations and cross-team coordination in a fast-paced administrative environment.
Developed foundational networking and cloud security expertise through structured mentorship with senior Cisco engineers.
Built data automation tools to reduce manual document processing and pricing errors for the operations team.
Built and deployed full stack features while optimising cloud infrastructure costs.
An automation engineering project focused on reliable, production-grade workflows. Currently working as an automation engineer.
The simplest solution that works is usually the right one. Complexity should earn its place.
A real system with real users teaches you more than a perfect plan ever will.
How something feels to use is not a finishing touch — it's a requirement from day one.
The field moves fast. Staying useful means staying a beginner at something, always.