AI infrastructure
Grounding, content enrichment, relevance experimentation, and the pipelines that carry trustworthy context into AI experiences.
Nairobi, Kenya · Engineering for global scale
Complex systems.
Clear outcomes.
I’m Evans, a software engineer working across platform engineering, distributed systems, and AI infrastructure. I build the foundations that help products stay useful, dependable, and easy to evolve.
01 / PRACTICE
Infrastructure is a product.
The best platforms hide complexity without hiding what matters. They give teams safe defaults, strong feedback loops, and room to move quickly.
My role often sits between architecture and delivery: clarifying the problem, aligning people across disciplines, building the critical path, and learning from production.
02 / DOMAINS
The layers I like to connect.
From data movement and schemas to the product experiences they enable.
Distributed platforms
Event-driven systems, sharded data flows, observability, and reliability work designed around real operating conditions.
Developer experience
SDKs, debugging surfaces, and type-safe tools that turn difficult platform concepts into coherent workflows.
API & schema systems
OpenAPI, CSDL, TypeSpec, validation, and generation systems that improve quality at the source rather than after release.
03 / SELECTED SIGNALS
A few outcomes, not the whole résumé.
Representative examples of how I frame and deliver platform work. The complete career record lives on the résumé page.
01
Making enterprise context useful to AI
Built enrichment and routing systems that connect content with the people it is relevant and permitted to reach. The work combined identity, relevance signals, experimentation, and production reliability across a large distributed pipeline.
366M references processed daily, with end-to-end delivery reduced from days to hours.
02
Moving correctness earlier
Created type-safe tooling and a public SDK that help teams describe APIs and AI agents with less error-prone authoring. The goal was not another abstraction—it was clearer intent, earlier validation, and more dependable downstream systems.
03
Giving developers a way to see the system
Shipped debugging and API exploration experiences that make orchestration, capability responses, and failures legible. Close partnership with design, product, and partner teams turned backend complexity into workflows used at meaningful scale.
04 / APPROACH
How I move work forward.
- 01
Find the real constraint
Trace the system, ask what customers and operators experience, and separate symptoms from the bottleneck worth solving.
- 02
Design for feedback
Make behavior observable and hypotheses testable so the platform can improve with evidence instead of intuition alone.
- 03
Build across boundaries
Translate between architecture, product, design, security, and operations to keep a shared outcome in view.
- 04
Leave leverage behind
Create tools, contracts, and patterns that help the next team solve the problem faster and more safely.
05 / NEXT
Let’s build the
hard part.
I’m interested in ambitious platform and product problems across AI infrastructure, distributed systems, reliability, and developer experience.