Backend & Applied AI Engineer
Building distributed systems, cloud control planes, and governed agentic workflows.

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I build product-focused systems where backend architecture, infrastructure, and AI have to work together reliably.

My recent work covers:

• distributed and event-driven backend systems
• cloud control planes and runtime lifecycle orchestration
• Kubernetes, GitOps, CI/CD, and observability
• policy-grounded RAG, guarded agents, workflow memory, and evaluation
• human-in-the-loop AI with traceable and governed decisions

I care about the engineering around the model just as much as the model call: validation, failure handling, idempotency, observability, evaluation, safety boundaries, and operational proof.

A governed agentic workflow that turns an unstructured motor-insurance claim into a policy-grounded, human-reviewed, and fully traceable case.

• document extraction and deterministic validation
• policy RAG with verified citations and abstention
• guarded tool-calling agent and human review
• safe workflow memory from trusted outcomes
• AI gateway, per-run traces, cost and latency visibility
• synthetic evaluation suites covering the complete workflow

Built with: TypeScript, Next.js, PostgreSQL, Prisma, pgvector, OpenAI, Bun, Turborepo, Docker

A control-plane-first cloud workspace platform that turns a browser request into a real code-server environment running on AWS.

• EC2 Auto Scaling Group allocation and idle VM reuse
• explicit runtime lifecycle and failure states
• Redis locks and runtime state mirroring
• VM agent and Docker-based workspace boot
• S3-backed project restore and synchronization
• browser dashboard exposing infrastructure state

Built with: TypeScript, Next.js, PostgreSQL, Redis, AWS EC2/ASG/S3, Docker, Clerk, Bun, Turborepo

A production-style uptime monitoring platform built around independent workers and Redis Streams.

• scheduled website monitoring and response-time history
• status-transition detection and incident lifecycle
• asynchronous notification pipeline
• authenticated user and admin dashboards
• Prometheus metrics and containerized local development
• Kubernetes deployment managed through a separate GitOps repository

Built with: Go, Gin, Redis Streams, PostgreSQL, Next.js, Docker, Prometheus

Deployment: runstate-gitops — Argo CD, Kustomize, External Secrets, ingress, Grafana, HPA, and automated image updates.

Languages: Go, TypeScript, JavaScript, Python, SQL
Backend: Gin, Node.js, REST APIs, background workers, event-driven systems
Data: PostgreSQL, Prisma, Redis, pgvector
Cloud & infrastructure: AWS, Docker, Kubernetes, Argo CD, Kustomize, GitHub Actions
AI systems: RAG, tool-calling agents, workflow memory, evaluations, tracing, human review
Frontend: Next.js, React, Tailwind CSS
Observability: Prometheus, Grafana, structured traces and model-call metadata

I am interested in backend, platform, cloud, and applied AI engineering roles where I can work on reliable product systems—not isolated demos.

I am especially drawn to teams building developer infrastructure, AI-enabled workflows, distributed services, internal platforms, or operational tooling.

Explore the repositories above for architecture diagrams, implementation walkthroughs, demos, evaluation results, and operational evidence.