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.