I build high-performance software and scalable infrastructure for AI data collection. I specialize in bridging the gap between high-level application requirements and low-level systems engineering, ensuring that data pipelines are reliable, performant, and capable of operating at scale.
Core Technical Stack
- Systems Engineering: Rust, C, Linux Internals, Concurrency
- Data Infrastructure: Distributed Systems, High-Throughput Pipelines, Scalable Backend Design
- Full-Stack: Python, TypeScript, FastAPI, Quarkus
- Engineering Interests: AI Systems, Performance Optimization, Memory Management, Networking
What I Bring
- AI Infrastructure Expertise: Currently at Digital Umuganda, where I design and maintain robust data collection systems that drive large-scale AI training.
- Systems-First Engineering: A relentless focus on how software interacts with hardware to maximize data throughput and minimize latency in high-demand environments.
- Full-Cycle Delivery: Proven experience architecting and scaling production-grade ingestion systems that reliably handle heavy, concurrent loads.
- Rapid Iteration: I specialize in high-velocity prototyping, diagnosing complex system bottlenecks, and re-architecting for stability.
Current Focus Advancing my work in systems programming and distributed architectures. I am currently building tooling designed to optimize efficiency and ensure the data integrity of massive-scale AI collection pipelines.