Technical Product & Platform Architect Munich: Full-Stack, AI & Delivery

Technical product, platform architecture, AI integration and structured delivery — backed by hands-on full-stack engineering. Work spans the path from product and business requirements to architecture, data, APIs, frontend implementation, AI, infrastructure, technical SEO and production operation.

The professional role is broader than a conventional Full-Stack Developer profile. Full-stack engineering remains a core execution capability, while the wider value lies in connecting technical product thinking, solution and platform architecture, project delivery, AI-enabled systems and operational implementation.

Turn product intent into a structured delivery path, a maintainable architecture and a working system that can evolve in production.— stajic.de

From Product Opportunity to Working Platform

Digital product work becomes more effective when requirements, project structure, architecture and implementation remain connected. The same applies after launch: deployment, search discoverability, observability, troubleshooting and continuous improvement influence the architecture from the beginning.

BUSINESS / PRODUCT NEED ↓
REQUIREMENTS ↓
PROJECT / DELIVERY STRUCTURE ↓
PLATFORM / SOLUTION ARCHITECTURE ↓
DATA / API / RBAC ↓
FRONTEND / APPLICATION ↓
AI / RAG / LOCAL MODELS ↓
INFRASTRUCTURE / DEPLOYMENT ↓
SEO / DISCOVERABILITY ↓
PRODUCTION / CONTINUOUS IMPROVEMENT

Services and Solution Areas

Engagements can focus on architecture and consulting, structured product and project delivery, implementation, or a combination of these layers. The objective is a clear technical outcome rather than selling isolated development hours.

Technical Product & Platform Architecture — translate product goals into scope, requirements, system boundaries, data models, APIs, permissions, integrations and operational design.
SaaS, Multi-Tenant & RBAC Architecture — design tenant-aware platforms, role and permission structures, multi-domain concepts, API boundaries and scalable application foundations.
AI Application Architecture — integrate cloud and local models, RAG, intelligent search, document workflows, model abstraction and controlled application-level AI capabilities.
Technical Delivery & Project Structuring — requirements, milestones, risks, stakeholders, acceptance criteria, backlog structure, validation, traceability and operational readiness.
Full-Stack Engineering — implement frontend, backend, APIs, databases, integrations and application services as an execution capability within the wider product architecture.
Platform Modernization & Replatforming — evolve existing systems toward clearer architecture, current frameworks, stronger data boundaries and maintainable production operation.
Technical SEO & Semantic Publishing Architecture — design multilingual, structured and discoverable platforms with canonical logic, hreflang, sitemaps, structured data and semantic content systems.
Infrastructure & Production Engineering — Linux, NGINX, Node.js runtimes, deployment, reverse proxies, diagnostics, recovery paths and production troubleshooting.

Platform, SaaS and Access Architecture

Platform architecture covers more than framework selection. Product domains, tenants, users, roles, permissions, data ownership, API boundaries, integrations, internationalization and deployment models need a coherent structure that supports both implementation and future product evolution.

SaaS and multi-instance platform concepts
Multi-tenant and multi-domain architecture
RBAC, roles, rights and permission boundaries
Application and service boundaries
Database and persistence models
REST and application API architecture
Authentication and authorization concepts
Internationalization and multilingual routing
Search, publishing and content architecture
Deployment topology and operational design

AI Applications with Local, Cloud and Hybrid Models

AI is treated as an application capability rather than as the product by default. The surrounding application layer defines data access, permissions, retrieval, provider selection, logging, user flows and operational behavior.

Practical work includes OpenAI and Codex integrations, Ollama, LM Studio, local Qwen-family models, local and remote model selection, provider abstraction, streaming, lifecycle handling, document ingestion and RAG-oriented retrieval.

Private and local AI assistants
Cloud AI integrations
Hybrid local and cloud model architectures
RAG and document retrieval
Semantic and intelligent search
Document ingestion and processing workflows
Provider-independent model layers
AI-assisted development and publishing workflows
Desktop AI clients and controlled local runtimes

Products and Productizable Platform Foundations

Independent engineering work is also directed toward reusable products and platform foundations. These assets are at different maturity levels and are presented according to their actual development status rather than as products with unverified market traction.

Aaasaasa AI Client — In Development

Desktop AI-client architecture for controlled local and remote model workflows. Current engineering includes Electron and TypeScript, secure IPC boundaries, managed sidecar processes, Codex integration, model/provider abstraction, streaming, lifecycle handling and cross-platform packaging.

AI-Enabled Multi-Domain Platform Foundation — Active Development

A reusable Nuxt-based platform foundation supports structured publishing and broader application use cases across multiple domains. Current architecture includes TypeScript/Nuxt, Prisma-based data access, PostgreSQL and additional database adapters, multilingual capabilities, security and authentication components, SEO tooling, structured EditorJS content and deployment-oriented workflows.

The same platform thinking supports the evolution of independent properties such as stajic.de, loving.rocks and figure.rocks and creates a foundation for future multi-brand, white-label or SaaS-oriented implementations where the product scope justifies them.

Nuxt 4 Multi-DB Prisma Starter Bridge — In Development

A reusable engineering foundation for Nuxt 4 applications with Prisma and multiple database backends. The project is designed as a starter bridge for PostgreSQL, MySQL and MongoDB environments and can support future platform, migration and integration work without rebuilding the same foundation for every implementation.

SenseFlow — Product Discovery

Independent product work currently developed through structured discovery, scope definition, assumptions, roadmap thinking, decision traceability and technical feasibility. The project remains clearly separated from unrelated course projects and is not presented as a finished commercial product.

Owned Platforms as Engineering and Product Evidence

Owned platforms demonstrate end-to-end responsibility across architecture, implementation, content systems, SEO, internationalization, deployment and continuous operation. They are also environments for testing product positioning, platform reuse and new technical capabilities.

stajic.de — primary professional platform, technical knowledge base and evidence layer for software engineering, architecture, AI and enterprise delivery.
loving.rocks — international wedding and lifestyle platform with multilingual publishing, semantic SEO Pillars and premium positioning concepts.
figure.rocks — gaming and technology platform combining gaming experience, hardware, software, technical guidance, performance and structured publishing.
bazify.com — broader commerce and platform project within the independent product portfolio.

Certified Delivery Capability

Formal project and Scrum qualifications reinforce the delivery side of a long-standing technical background. IPMA® Level D – Certified Project Management Associate supports structured project planning and execution, while Professional Scrum Master I (PSM I) supports empirical product delivery and Scrum practice.

Requirements and scope structuring
Stakeholder communication
Milestones, roadmaps, risks and opportunities
Acceptance criteria and validation
Backlog, epic, issue and task structures
Architecture decisions and technical documentation
Change and delivery coordination
Release and operational readiness

Jira, Confluence and Git as Delivery Infrastructure

Jira is used for backlog, epics, issues, workflows, acceptance criteria and delivery tracking. Confluence provides the structured knowledge layer for requirements, architecture, decisions, roadmaps, risks, traceability and product documentation. Git and GitHub connect these delivery structures with version-controlled implementation.

Hands-On Full-Stack Engineering

Full-stack engineering remains a central implementation capability and provides the ability to carry architecture into working software. Current platform development commonly combines TypeScript, Vue, Nuxt, Node.js, Nitro, Prisma and PostgreSQL.

Frontend: JavaScript, TypeScript, Vue, Nuxt, HTML, CSS
Backend & APIs: Node.js, Nitro, PHP, Python, Django and API integrations
Data: PostgreSQL, Prisma, MySQL, MongoDB, SQLite and Solr
Infrastructure: Linux, NGINX, Apache, PM2, Docker and Git
Interactive systems: Three.js and OpenSeadragon
Publishing & Search: EditorJS, multilingual architecture, technical SEO and structured content

Performance, Technical SEO and Discoverability

Performance and search discoverability are treated as platform concerns. Rendering strategy, information architecture, URLs, metadata, multilingual routing, structured data, internal linking, sitemap design, image delivery and server behavior all contribute to the final product.

This is especially relevant for content-rich and international platforms where SEO, AI-assisted search visibility and semantic content architecture need to remain connected to the underlying application and data model.

Primary-Source Technical Knowledge and Case Studies

stajic.de also functions as a technical knowledge base for architecture decisions, implementation experience, production incidents, migration work, AI integration and reproducible engineering findings. The purpose is to make practical technical evidence available alongside service and product work.

Commercial Engagement Models

The combination of architecture, delivery and implementation supports several forms of collaboration depending on the required outcome.

Architecture & Consulting — product architecture, platform assessments, technical roadmaps and AI architecture.
Discovery & MVP / PoC Delivery — structure an opportunity into requirements, architecture, backlog and a validated implementation path.
Implementation — full-stack, data, API and AI engineering for defined product capabilities.
Platform Modernization — architecture renewal, migration, technical SEO preservation and production hardening.
Private / Local AI Deployment — local or hybrid AI application architectures for controlled data and model workflows.
Reusable Platform Implementations — adapt productized platform foundations for suitable multi-domain, multi-brand or structured publishing use cases.
Production & Evolution — deployment, troubleshooting, optimization, documentation and continuous platform improvement.

Technical Product and Platform Work in the Munich Area

Based in Taufkirchen near Munich, collaboration can be organized locally in the Munich area or through distributed delivery. The working model remains the same: clear requirements, traceable decisions, maintainable architecture, version-controlled implementation and production-oriented delivery.

A broader overview of architecture, AI, delivery and platform capabilities is available on the Services page.

Professional history, certifications, technology background and selected projects are available in the Curriculum Vitae.

Move from Opportunity to a Working Product

A strong technical product combines a clear target, structured delivery, suitable architecture and implementation that can operate and evolve in practice. The next step can be an architecture assessment, product discovery, implementation plan, AI integration or a complete path toward a working platform.

Direct contact: info@stajic.de