Open-Source Software Engineer
Worked on Meshery and Kubernetes discovery flows, including streaming-list optimizations, CRD and operator improvements, test stability, and resource synchronization behavior across cluster environments.
About Daniel
I am a software engineer focused on backend systems, Kubernetes, and the kind of infrastructure work where performance, reliability, and clear design all need to hold up at the same time.
Over the last 6+ years, I have worked across fintech, analytics, and developer tooling, building systems used by large active user bases and engineering teams that depend on software behaving well in production, not just in local demos.
I enjoy work that sits close to real operational constraints: API behavior under load, distributed processing, Kubernetes resource synchronization, and platform decisions that make future delivery simpler instead of harder.
Snapshot
MSc Artificial Intelligence, Distinction
Certified Kubernetes Administrator
Go, Python, C#, JavaScript, Kubernetes, AWS, Azure
What this page is for
The home page is the quick overview. This page is the deeper version: experience, context, education, and the through-line behind the systems work I care about.
How I got here
My path into software has always leaned toward complex systems. Earlier work in fintech put me close to transactional integrity, operational risk, and high-availability services. Later roles pulled me further into data pipelines, internal tools, and cloud-native platform work, where throughput and observability mattered just as much as product speed.
That progression naturally led into Kubernetes and infrastructure-focused engineering. I find that environment especially compelling because it rewards both technical depth and good judgment. Small implementation details can have large system-wide consequences.
Selected experience
Senior Software Engineer
Led engineers building internal tools and distributed pipelines, improved CI/CD, owned production services, and delivered systems used daily by more than 10,000 users.
Software Engineer
Built financial microservices, supported cloud migration to Azure, and helped reduce transaction latency using event-driven workflows and resilient backend services.
Education and achievements
MSc Artificial Intelligence
Nottingham Trent University
Distinction
- Focused on production-minded AI systems, performance, and memory-aware design.
- Completed research on lightweight super-resolution for edge and drone-based systems.
BSc Electrical and Electronics Engineering
University of Nairobi
First Class Honours
- Built a strong foundation in systems thinking, engineering rigor, and analytical problem solving.
What I am focused on now
I am especially interested in systems that blend sound backend engineering with modern AI capabilities without losing practicality. That means paying attention to latency, resource use, resilience, and the quality of the operator or developer experience around the system, not just the core model or service itself.
Projects and contributions
Open-source contribution
Meshery Kubernetes Discovery Improvements
Contributed to Meshery and MeshSync discovery flows, improving Kubernetes resource tracking, streaming-list performance, and cluster synchronization behavior.
- Extended discovery mechanisms for more accurate resource visibility across environments.
- Improved performance by using Kubernetes Discovery APIs and streaming-list operations.
- Strengthened test coverage and CI reliability around critical synchronization flows.
Project
Config-Op Kubernetes Operator
Designed an operator to synchronize ConfigMaps and Secrets across namespaces with a focus on reliability and consistency in multi-service production environments.
- Built for repeatable configuration propagation across Kubernetes workloads.
- Focused on operational simplicity and safe synchronization behavior.
- Shaped as a practical tool for teams managing shared configuration at scale.
Research project
Lightweight Super-Resolution on Edge Devices
Explored real-time image enhancement models for constrained edge and drone-based systems, balancing perceptual quality with memory and latency constraints.
- Optimized for real-time execution on resource-constrained hardware.
- Focused on practical trade-offs between quality, throughput, and memory use.
- Connected machine learning work with deployable engineering concerns.
Certificates
Certified Kubernetes Administrator (CKA)
The Linux Foundation
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Microsoft Azure Fundamentals (AZ-900)
Microsoft
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Microsoft Azure AI Fundamentals (AI-900)
Microsoft
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Technical toolkit
Languages
- Go
- Python
- C#
- JavaScript
Infrastructure
- Kubernetes
- Docker
- AWS
- Azure
Data and systems
- PostgreSQL
- Redis
- REST APIs
- SQS/SNS