Bio
Bio
I’m a senior backend engineer with 5+ years of experience specializing in Go and cloud-native systems.
I design, build, and operate production systems with a strong focus on reliability, performance, and clear system boundaries. I work close to real infrastructure — Kubernetes, CI/CD, observability, incident response, and platform configuration — and care deeply about making systems understandable and predictable.
I also integrate AI-driven components into backend systems where they add real, measurable value.
What I work on
- Designing and operating Go-based backend systems in production
- Building microservices and APIs with clear boundaries and predictable behavior
- Cloud-native systems using Kubernetes, Docker, Helm, GCP, Azure, and CI/CD pipelines
- Owning platform configuration through GitOps, Helm charts, and governed configuration repositories
- Observability-first engineering: metrics, structured logs, and tracing
- Integrating AI components (LLMs, RAG, agents) into real backend workflows
How I approach building
- Prefer simple, explicit architectures over clever abstractions
- Treat production behavior as the ultimate truth
- Design systems to be observable, debuggable, and predictable
- Integrate AI where it improves outcomes, not where it adds complexity
- Optimize for long-term maintainability, not short-term speed
Core skills
Languages & Backend
Distributed Systems
Data & Messaging
Cloud & Platform
CI/CD & Observability
AI Systems
Experience
- Senior Software Developer at Capgemini since January 2025
- Platform Configuration Owner since March 2026, accountable for configuration repositories, Helm charts, CI/CD pipelines, GitOps maintenance, and QA test setup
- Designed and delivered Go-based services running in Kubernetes environments
- Owned service reliability, release pipelines, deployment processes, on-call work, and incident response
- Introduced Prometheus metrics, structured logging, and distributed tracing to improve incident triage
- Contributed to OpenKCM with modular APIs and extensions
Selected work
- SmartLearn AI — An LLM-powered learning application using retrieval-augmented generation, embedding-based semantic search, context retrieval, and prompt orchestration.
- RitualOps AI — A system for orchestrating AI-driven workflows and long-running processes, with modular task execution, state management, inter-step coordination, and multi-tenant configuration.
- Operational backend platforms — Backend systems automating provisioning and operational workflows, built with Go, clean architecture, and cloud-native deployment patterns.
Research & writing
I explore how intent, context, and structure can make AI systems more predictable and explainable.
A proposed intent- and context-driven communication model for multi-agent AI systems, focused on predictability, coordination, and explainability beyond tool-centric approaches.