ogtechdev — Portfolio of systems, AI, and product work.

Bio

I’m a senior backend engineer specializing in Go and cloud-native distributed 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 — deployments, observability, and failure modes — 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 containers, orchestration, and CI/CD pipelines
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

Go (advanced)TypeScriptPythonSQLBackend APIs (REST / RPC-style)Concurrency and async processing

Distributed Systems

MicroservicesClean architecture and modular designReliability under loadFailure handling and system boundaries

Data & Messaging

Relational databases (PostgreSQL, MySQL)Caching (Redis)Messaging and async workflows

Cloud & Platform

Containerized servicesKubernetes-based deploymentsCloud platforms (vendor-agnostic mindset)

CI/CD & Observability

Automated pipelinesSafe and repeatable deploymentsMetrics, logging, tracingProduction debugging and operability

AI Systems

LLM integrationRetrieval-Augmented Generation (RAG)Agent-based and workflow-oriented architectures

Experience

Several years of professional experience building and operating backend systems in production
Designed and delivered Go-based services running in containerized, orchestrated environments
Owned service reliability, release pipelines, and deployment processes
Introduced observability practices that improved debugging, incident response, and day-to-day operability
Acted as a technical owner for backend platforms coordinating work across engineering, QA, and product
Contributed to open-source systems using modular APIs and plugin-based extensions

Selected work

ExplainIQ / SmartLearn AI — An LLM-powered learning platform using retrieval-augmented generation (RAG). Focused on explanation-first outputs, semantic retrieval, and response quality control.
RitualOps AI — A platform-agnostic architecture for orchestrating long-running AI and agent-based workflows, designed with modularity, intent awareness, and multi-tenant readiness in mind.
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.

Intentional Context Networks (ICN)

A proposed intent- and context-driven communication model for multi-agent AI systems, focused on predictability, coordination, and explainability beyond tool-centric approaches.