Cloud DevOps Infrastructure on Azure
Production-grade microservices infrastructure on Azure Kubernetes Service with fully automated CI/CD pipelines, achieving 99.9% uptime and cutting deployment cycle time by 50%.
Former Software Engineer, now pursuing MSc Applied AI at Warwick. Making ML systems that work in production, not just in notebooks.
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If you're building something interesting and need someone who gets both engineering and AI, let's talk.
Explore projects where I've delivered measurable business impact through scalable technical solutions.
Building backend systems in production
Université Evangélique en Afrique
Kikapu App
Digital Vision EA
Tools I use to build systems
After three years building backend systems in production, I decided to go back to school for my MSc in Applied AI at Warwick. Honestly, it felt like the right move—I wanted to understand ML at a deeper level, not just use it.
In industry, I worked with Spring Boot, NestJS, and Azure. Built APIs that handled 30K+ users with response times around 300ms. Spent a lot of time optimizing deployments, ended up cutting the cycle time by half.
Now I'm trying to combine that ops mindset with AI. The goal isn't just training models—it's making them work reliably when real people depend on them.
Looking for AI Engineering roles where I can apply production engineering experience and research skills.