Senior Software Engineer · Backend & Platform · Systems Integration · AI Engineering
10+ years building production software, APIs, integrations, data systems and infrastructure
Nairobi, Kenya · Open to senior/lead engineering and global remote roles
Senior software engineer with 14+ years designing and building production systems: backend services, APIs, enterprise integrations, mobile apps, data platforms and infrastructure.
My strongest area is the space between application code and real-world systems: integrating services, designing APIs, moving data reliably between systems, processing async workloads, automating workflows and keeping production platforms observable and maintainable.
I work across PHP/Laravel, .NET, Java/Spring, Python, PostgreSQL, SQL Server, Docker and Linux, from application code to architecture and production infrastructure.
More recently I've been applying LLMs to practical engineering and domain problems: structured outputs, validation, workflow automation and integrating AI into existing systems rather than treating it as a standalone experiment.
- Production REST APIs and backend services
- Service architecture, distributed systems and system-to-system integrations
- Queue-based and asynchronous processing, background jobs, workflow orchestration
- Payment and transactional integrations
- Data pipelines and structured data processing
- Authentication and API security
- Performance, reliability and production troubleshooting
A big part of my work has been connecting systems that were never designed to work together: third-party APIs, payment systems, enterprise apps, messaging and queues, databases, webhooks and callbacks, scheduled jobs, and the data transformation and validation between them.
I enjoy the unglamorous problems: reliability, retries, idempotency, failure handling, monitoring and operational visibility.
About a year of hands-on AI engineering, focused on integrating AI into existing software and business workflows. Practical rather than model-centric:
- LLM API integration, structured outputs, prompt design and evaluation
- Data extraction, transformation and validation of AI-generated data
- LLMs wired into downstream APIs and databases
- Local LLM experimentation and AI-assisted developer tooling
Example: turning farmer training field guides into structured, validated data for downstream systems, combining LLM output with conventional validation and workflow processing.
I care about systems beyond getting an endpoint to return 200 OK: service boundaries, API contracts, retries, idempotency, database design, caching, connection pooling, observability, failure isolation, containerisation and operational simplicity.
For small and medium systems I prefer boring technology that's easy to operate over distributed infrastructure for its own sake. When complexity is justified, I'm comfortable reasoning about service boundaries, async processing, integration patterns and operational trade-offs.
Production Laravel platform covering APIs, async processing, SMS workflows and callback-heavy integrations.
- Laravel, Sanctum, Horizon and queue workers
- PostgreSQL (with PgBouncer) and SQL Server integration, Redis
- Dockerised deployment behind Caddy
- Prometheus/Grafana monitoring and Loki log aggregation
- Async workloads: API logging, SMS processing, campaigns, callbacks, background data processing
Software supporting agricultural decision-making and farmer-facing workflows at IITA. Data quality, usability, domain rules and downstream integrations matter as much as the code.
REST services, payment-related systems, messaging, database-backed apps, workflow automation and data transformation. I've used Apache Camel and also built equivalent integration patterns directly in application platforms.
Languages
Backend
Databases & data
Infrastructure & DevOps
Observability: Prometheus · Grafana · Loki · Grafana Agent · production diagnostics
Workflow & integration: n8n · Apache Camel · Laravel Horizon · REST · webhooks · queues · scheduled jobs
AI: LLM APIs · structured generation · prompt engineering · Ollama / local LLMs · AI-assisted development
Simple → Observable → Reliable → Maintainable → Scalable
Not every system needs microservices, Kafka or Kubernetes. A well-built Laravel app with queues, Redis, PostgreSQL, Docker and good observability often beats a pile of unnecessary services.
AI engineering and LLM-powered apps · AI-assisted development · workflow automation · local LLM infrastructure · modern Laravel architecture · Python, .NET and Java/Spring services · containerised deployments · observability · distributed application patterns
Home lab: Docker, WSL, Caddy, n8n, Jellyfin/Plex, Pi-hole, Ollama and local AI coding tools.
Android (Kotlin/Java), mobile/backend integrations, .NET services, data analysis in R, Linux and shell automation, legacy PHP and enterprise integration platforms. Modern systems rarely live in one stack, and that breadth has paid off.
Roles: Senior / Lead Software Engineer · Backend Engineer · Platform Engineer · Software Architect · AI Engineer / AI Lead
Teams building: developer platforms · SaaS · financial/payment systems · integration-heavy platforms · AI-enabled products · data-intensive apps · automation platforms · infrastructure tooling
Open to senior IC and technical leadership roles.
Independent software engineering and consulting via Munywele Consulting:
Backend/API development · modernising existing systems · third-party integrations · Laravel · .NET/Java services · database architecture · queues and background processing · Dockerisation · production troubleshooting · workflow automation · AI/LLM integration
I like understanding how things actually work: cars, mechanical systems, electronics, infrastructure, anything with enough moving parts to make debugging interesting. And yes, I develop recipes too 🍳