Systems Engineer

Control layers for AI systems, not just model wrappers.

I build Python services focused on FastAPI, MCP-aware backend logic, runtime governance, operator-facing tools, business workflows, and practical AI integrations.

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public projects spanning AI runtime governance, MCP, RAG, and backend systems

MCP

backend-first profile with runtime policies, approvals, traces, and operator tooling

AI Ops

practical AI systems work with real control, observability, and infra story

Selected Projects

Projects that actually prove the profile.

FastAPI Agents PostgreSQL + Redis

Agent Runtime Control Tower

FastAPI control tower for AI agents with policy enforcement, approval routing, execution traces, blocked-run incidents, PostgreSQL-backed history, and Redis-backed runtime state.

Open repository

FastAPI Workflow Governance Operator Tools

Automation Control Plane

Backend-first control layer for automation workflows with tenant auth, approval routing, usage limits, execution audit logs, and an operator dashboard for execution governance.

Open repository

OpenAI MCP Developer Tools

agent-intel-mcp

OpenAI-powered MCP server and demo dashboard that scans GitHub repositories, extracts agent-engineering patterns, compares them against a local codebase, and proposes safe AGENTS.md improvements.

Open repository

FastAPI Scheduling Multi-tenant

Salonos

Booking and visit management backend with reservation flows, tenant routing, reports, exports, and Telegram bot integration.

Open repository

FastAPI Business APIs Automation

Danex Business API

Backend for business workflows, admin operations, auth, reporting, and operational automation. Best representative project for Python backend roles.

Open repository

Python Feedback Intelligence Reporting

Brand Insight Engine

Feedback intelligence pipeline that turns public customer feedback and competitor mentions into structured product and marketing insights with deterministic analysis and clean reporting output.

Open repository

RAG LangChain FAISS

Danex RAG Service

Hybrid retrieval service that combines vector search with SQL-backed answers for operational and reporting use cases.

Open repository

Telegram Google Sheets TeamOps

Salon Utarg Bot

Telegram operations bot for revenue tracking, financial reporting, cash-shift workflows, and team operations synced with backend systems.

Open repository

Stack

What I actually use.

Python FastAPI SQL PostgreSQL SQLite Docker Git Pytest Redis MCP LangChain FAISS REST APIs Bash

Primary direction

Python backend, API design, runtime governance, automation, and testable business logic.

Secondary edge

Practical AI systems: MCP-aware services, retrieval pipelines, LLM-assisted workflows, internal tools.

What I am not selling as

Backend-first systems profile. Not a generic fullstack pitch and not a pure data-science profile.

Positioning

How I fit on the market right now.

Best target roles

Systems Engineer, Backend Platform Engineer, Python Backend Engineer, AI Infrastructure Engineer.

Best company fit

Product teams, internal platform groups, and operational tooling environments where backend work and runtime safety matter.

Working style

Remote-first or hybrid, product logic, integrations, testing, automation, reliability, and operator-facing systems.

Contact

Want the shortest path to the relevant proof?

The fastest way to evaluate me is through GitHub repositories and a short technical conversation around Python backend work, FastAPI services, MCP-aware systems, runtime governance, and practical AI integrations.