GPT Experts in Germany

Stabilizing an Azure/.NET landscape in live operation.

• Architecture, DevOps, and operational readiness; technical decisions under time pressure
• Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics

Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps

• Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
• Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
• Development of robust REST APIs for automated document processing and system integration
• Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
• Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
• Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
• Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes

Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation

• Task Engineering: Evaluating Coding Agents.
• Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
• Prompting & Translation: finding failure points where AI does not work, in German.
• Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
• Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
• Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
• Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.

With project PRIZMA, DB will modernize its infrastructure on the one hand, and develop a fail-safe IT landscape on the other hand, which can be restored quickly and securely in case of a disaster.

• Capture current architectures of existing systems as well as methodical consulting and development of target architectures
• Deepen and maintain the building plan / target IT landscape
• Implement technical architecture concepts & architecture descriptions
• Implement migration concepts for updating and further developing the platform and information systems
• Assess submitted improvement suggestions as part of the project
• Capability management: identify capability gaps, develop target visions, and support transformation planning within the enterprise architecture.
• Create a compatibility matrix of the components in use and compare dependencies of specific versions
• Create an IT concept for extending the platform with the following topics: hardware and software requirements, security, licensing, high availability, load balancing, backup & recovery, update strategy, monitoring integration, etc.
• Coordinate with business architects as well as technical architects from the cross-functional architecture area of the PRISMA program for the topics (backup, Active Directory, monitoring, Citrix, and business applications ...)
• Status meetings and alignment of project planning with the Release Train Engineer / Project Manager
• Advise the Release Train Engineer / Project Manager in identifying project risks
• Advise the System Architect Engineers in steering the implementation of the concept
• Implement the IT concept
• Document the infrastructure

Label: MS Project, LINUX, Windows, ORACLE, Java, REST, SharePoint, Microsoft Exchange, UML, Enterprise Architect, BPMN, AZURE, AWS, V-MODEL, Micro Service, VisualStudio, SAP S/4HANA, SCRUM(SAFE), ESB (TIBCO), Python, Innovator, LeanIX (TOGAF), Ansible, Ansible Tower, Ansible Automation, ROBOT, SpringBoot

• Consulting across two integrated areas: Commercial Strategy (pricing, sales steering, marketing strategy, market expansion, margin management) and Operational Efficiency (process automation, AI integration, workflow design, last-mile automation).
• Development of custom SaaS solutions, explicitly tailored to the specific requirements and processes of each company.
• Delivery of AI training and change management workshops for managing directors and specialist departments, including AI competence training with a certificate of attendance under Art. 4 of the EU AI Act.

• Goal/Motivation: PAYONE urgently needs a 360° view of its customers. So far, PAYONE has no overall master data strategy. It is not possible to identify customers across all relevant systems.

The organization is to be enabled to identify customers across all relevant systems. Creating the foundation for master data management at PAYONE

• Challenge: Due to acquisitions, the system landscape is very heterogeneous. The company is very dynamic and burdened with many system harmonization and integration projects, so resource bottlenecks and changes in project priorities are again and again almost impossible to handle.

Due to BaFin findings, the project has a central task and role. The first focus is on migrating all customers from the master-data-leading backend systems with their AML/KYC data to Salesforce. This is intended to resolve one of the largest findings and establish the corresponding ODD/EDD processes.

In addition, customer data must be harmonized in Salesforce. Previous migrations led in some cases to duplicate customer records. In the end, only one customer should be maintained in Salesforce and, with the corresponding information from the backend systems, it should also be possible to recognize which products and in which processing systems the customer uses Payone services.

Project: ONE Customer

Budget: €1.5 million

Team: 10/30 employees (full-time/part-time); 4 vendors/providers

Integration: 8 (subsystems/interfaces)

Applications: Salesforce; SAP S4/HANA; custom developments

Tools: MS Office; Jira, Confluence, SharePoint

Methods: Hands-on; Agile (SAFe); Prince2

• Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
• Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
• Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
• Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.

Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy

I founded Nick's Advisory to make HR management, HR project delivery, and AI expertise available to companies on a temporary basis when internal capacity or experience is lacking.

Services:

• HR interim management: taking over the HR business partner or HR manager role during restructuring, growth, or a vacant leadership position, on a daily or hourly basis
• HR consulting: projects around reorganization, talent acquisition, separation processes, and building HR structures
• AI consulting for HR teams: selection, introduction, and use of AI tools in HR processes

Testing and verification of high-voltage systems

• Performed integration and system tests for control units in PHEV/EV vehicles using ECU-TEST (TraceTronic), Vector CANoe, CANalyzer, ETAS INCA, Tornado, E-Sys, and EDIABAS.
• Analyzed the interaction of high-voltage control units (including CCU, BMU, inverter, IPB, and IPF) and carried out software updates and flash processes to verify new software versions.
• Worked closely with software, system, and integration teams in an agile development environment to analyze issues and verify new software versions.

• TrendReel, production LLM agent and RAG system (Python, LangChain, OpenAI, Groq/Llama 3, Claude, FastAPI, Kubernetes, PostgreSQL).
• Designed and built a production multi-step LLM agent system: a script generation agent with a per-platform psychology database, 7 viral narrative frameworks, and structured quality scoring, switching between Claude and Groq backends in real time based on output metrics.
• Implemented multi-provider LLM routing (Claude primary, Groq/Llama 3 fallback) with priority-chain failover and quality-based provider switching, achieving 95% inference cost reduction while holding measurable quality thresholds.
• Built an advanced RAG-style retrieval pipeline with per-platform knowledge bases, semantic content matching, and structured output evaluation across 7 decision frameworks, directly analogous to multi-tenant context-based reasoning for enterprise document workflows.
• BrainyAI, adaptive AI learning platform (Python, LangChain, Groq Llama 3.3-70B, OpenAI, Next.js, Supabase, Redis).
• Integrated Groq Llama 3.3-70B with education-level-aware prompting, dynamically adjusting vocabulary depth, citation complexity, and reasoning style across four student proficiency tiers.
• Nexus Prime, multi-tenant SaaS platform for marketing and growth automation (25 modules, 99 backend routers, 153 frontend files).
• Built a 25-module, 99-router multi-tenant SaaS platform covering ad remix, affiliates, WhatsApp inbox, email, and cart recovery, serving four subscription tiers from $199 to $1,999 per month with integrated Stripe, Paystack, and Flutterwave billing.
• AI Video Surveillance Platform, multi-tenant edge and cloud computer vision system currently in active client pitch.
• Designed a multi-tenant AI video surveillance platform combining edge YOLO26 inference on NVIDIA Jetson Orin NX boxes with a central GKE cloud layer (Postgres, Pub/Sub, ClickHouse, R2, Keycloak) for event storage, dashboards, alerting, and multi-tenancy.

• AI-native B2B SaaS for freelancer sourcing; DACH market.*

• Enterprise partnerships across four industries: structured and closed multi-stakeholder deals with Telefónica (Telco), Emma Matratzen (Retail), Nürnberger Versicherungen and Flatex (Financial Services), Hubert Burda Media and Serviceplan Gruppe (Media).

• Revenue and growth: scaled FRATCH from €0 to €3.8M annual GMV, with ~80% of revenue sourced from founder-led direct outreach and partner relationships.

• Channel partnerships: sold FRATCH as a SaaS solution to recruiting firms (e.g., YER) — built the partner-enabled motion alongside direct enterprise sales.

• Team build: scaled FRATCH from solo founder to a team of 7 across engineering, product design, operations, and supply outreach.

• Proprietary network asset: onboarded 15,000+ freelancers as registered users — the proprietary DACH network powering FRATCH's matching.

• Built and launched FRATCH GPT (fratch.io/gpt): a production conversational AI agent. Architected the full stack — LLM orchestration, embeddings, re-ranking — with hands-on involvement in technical design and execution.

• GTM build: owned the full go-to-market stack — outbound, LinkedIn (organic + paid), content, and sales enablement.

• Administration and further development of the M365 environment including Teams, Exchange Online, Intune and SharePoint Online
• Development of dashboards for performance monitoring using Microsoft Power BI
• Implementation of user requirements and processes with low-code/no-code technologies (Power Apps, Power Automate)
• Use of Jira and Azure DevOps to plan and implement user stories in an agile project context
• Participation in daily stand-ups, sprint planning, sprint reviews and retrospectives as technical point of contact
• Planning and implementation of security policies using Azure Conditional Access and multi-factor authentication
• Configuration and deployment of clients and applications via MECM (SCCM) and Baramundi

• Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices