I build practical Python/FastAPI backend systems around AI governance: agent workflows, scoped tool access, policy simulation, approvals, audit logs, redaction, incidents, observability, regression testing, and operator-facing control layers.
My experience combines real-world internal business engineering through DANIELOZA.AI, production-shaped GenAI/backend projects, and AI automation work across internal workflow domains. The common thread is backend systems that are reviewable, auditable, secure, and useful in practical business operations.
- AI governance platforms with scoped access, policy simulation, approvals, incidents, replay, traces, and operator workflows
- RAG backends with ingestion, route transparency, citations, and evaluation surfaces
- Security and policy layers around agent/tool execution
- FastAPI backends with SQL, Docker, testing, and production-minded structure
- Operator-facing internal tools instead of one-shot AI demos
| Period | Focus |
|---|---|
| Aug 2021 - Sep 2022 | IT operations, reporting, customer-facing support, ticketing workflows, SQL basics, and internal technical processes. |
| Oct 2022 - Oct 2023 | Enterprise IT automation, chatbot-related solutions, SQL/API-style workflows, documentation, troubleshooting, and team-based technical delivery. |
| Nov 2023 - Feb 2024 | AI automation prototypes and business workflow systems, including Brand Insight Engine and Automation Control Plane. |
| Mar 2024 - Mar 2025 | Team-based AI backend and internal agent-style workflow systems: controlled tool access, approvals, audit logs, observability, and human-in-the-loop review. |
| Apr 2025 - Present | DANIELOZA.AI backend and AI automation work for small and mid-sized businesses around Wroclaw, including Danex operations tooling, reporting, invoice workflows, Telegram operations, exports, backups, and AI/OCR-assisted processing. |
Internal Salon Operations Platform
Through DANIELOZA.AI, I build and maintain backend and automation systems for Danex and other small to mid-sized local service businesses, used in daily operations. The systems support appointment workflows, invoice handling, daily revenue tracking, reporting, Telegram-based operations, CSV/PDF exports, backup workflows, and practical AI/OCR-assisted document handling.
I also designed and published the Danex public website: www.salondanex.pl.
This is real-world engineering with real users, operational needs, direct feedback, maintenance responsibility, and business constraints.
| Project | What it proves |
|---|---|
| AGIP - Agentic Governance Intelligence Platform | Flagship AI governance platform for autonomous agents: scoped access, policy simulation, approvals, audit logs, redaction, incidents, regression testing, observability, inference readiness, and enterprise governance layers before agents touch business systems. |
| MCP Security Gateway | Security-first FastAPI gateway for MCP servers with deterministic policy checks, approvals, redacted audit logs, and incident creation. |
| Automation Control Plane | Backend-first FastAPI control layer for automation workflows with tenant auth, approval queues, usage limits, audit logs, and operator review surfaces. |
| Agent Control Plane | Operator-facing FastAPI control plane for AI runs with approvals, incidents, replay compare, trace graphs, exports, and reliability notes. |
| Agent Governance Gateway | Focused governance gateway with registration, human approval, short-lived scoped tokens, revocation, multi-tenant isolation, PII redaction, audit logs, policy checks, and a premium dashboard. |
| Danex RAG Service | Product-style hybrid RAG service with ingestion, route transparency, citations with scoring, query history, evaluation summary, and knowledge-base management. |
| Brand Insight Engine | Feedback intelligence pipeline that turns public customer feedback and market signals into structured product and marketing insights. |
| AI Workflow Observatory | Local-first observability dashboard for AI-assisted engineering workflows with sessions, risk signals, verification quality, and cost visibility. |
Python backend engineering
FastAPI and REST APIs
SQL, SQLite, PostgreSQL concepts
RAG pipelines and ingestion
Agent governance and control surfaces
AI runtime safety and observability
Approvals, audit logs, governance
Operator-facing internal tools
Python FastAPI SQLAlchemy SQLite Docker
Pytest RAG FAISS LangChain REST APIs MCP
TypeScript React Tailwind CSS
I am strongest in systems that need clear control surfaces: ingestion, retrieval, approval flows, runtime visibility, auditability, failure handling, and backend logic that stays understandable as the product grows.
I optimize for software that is useful in production-adjacent settings, easy to reason about, and structured well enough to evolve without collapsing into chaos.