Azure AI Foundry Experts in Germany

• Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
• Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
• Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.

• 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.

I designed and developed an AI-powered multi-tenant platform on Azure that transforms SAP process recordings into technical documentation, presentations and automated tests, processing over 15,000 process recordings for enterprise customers like Würth. I owned the architecture, the production releases and the DevOps setup. I also designed a multi-tenant system with SSO and role-based access on Azure. Implemented an MCP Server with Dynamic OAuth Authentication.

Main Tasks:

• Sprint planning and feature preparation
• Design the multi-tenant platform architecture (FastAPI, SQLAlchemy, PostgreSQL row-level security for tenant isolation)
• Develop AI pipelines with Prefect for transcription (Azure Speech API), document generation and SAP screen-recording analysis (Claude, gpt-4-mini)
• Design and implement an MCP server to expose tenant knowledge to LLM clients (Claude), with async retrieval and reranking
• Implement LLM cost tracking, rate limiting and client pooling for Anthropic/OpenAI/Azure OpenAI endpoints
• Set up CI/CD: Docker images to Azure Container Registry, GitHub Actions, Azure Static Web Apps, Alembic migrations in containers
• Manage production releases and execute live data migrations for enterprise customers
• Define engineering standards and architecture patterns for the team

Environment: Azure / Azure Foundry / Python / FastAPI / Prefect / React / PostgreSQL

Technologies: n8n, Notion, OpenAI API, Claude, MS AI Foundry, MS CoPilot Studio, MS CoPilot, LLM, Node.js, Vercel, LangGraph, PostgreSQL, pgEdge, pgvector, Docker, LangChain, Ollama, Open WebUI

• Continuous evaluation and prioritization of internal automation needs
• ~20 AI agents in active use: research, content pipelines, document processing
• 5 n8n workflows for automated data and process control
• Architecture built on the same principles as in customer projects: state management, event-driven orchestration, API integration
• Ongoing operation and further development

• ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.

• Development of a SaaS application for automated claims handling with AI agents (LLM) as the primary development team
• Design & testing of efficient and secure context management setups in the development process (including multi-sub-agent use, memory systems, caching)
• Definition and implementation of LLMOps pipelines with Azure AI Foundry for AI agents in customer contact (including versioning, logging, audit trail, security tests)
• Infrastructure provisioning via IaC (Bicep), application configuration via GitOps-based CI/CD pipelines (rules engine, workflow engine)
• Development of integrated security architecture designs between AI-based & classic applications with a special focus on regulatory requirements
• Integration of workflow and rules engine in a NestJS service architecture — for automated, rule-based control of claims processes
• Probabilistic extraction and preparation of claims data as the basis for rule-based, deterministic decision logic — traceable, auditable, and regulatorily compliant

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

OpsWorker turns Kubernetes alerts into root-cause analyses, on top of the monitoring a team already runs. I lead the technical side: the agent architecture, the AWS infrastructure it runs on (fully inside EU regions), and the engineering decisions behind it, read-only in the cluster by default, human in the loop for judgment. The stack underneath: Amazon Bedrock and Bedrock AgentCore, agents built with the Strands Agents SDK, the Claude and OpenAI APIs, and the Kubernetes API.

• Delivered ML pipelines for OCR, semantic search, and computer vision
• Integrated Azure AI Agents and GPT workflows for automation and QA
• Deployed cloud-based FastAPI services with scalable architecture
• Created integration docs and advised on LLM production readiness

• New development of project room functions
• Connection of REST API of a self-developed web service (.NET 7/8) as Azure App Service
• UI tests with Playwright
• Extension of Azure DevOps pipelines
• Migration to Azure SQL Server
• Software / technology: SharePoint Online, PowerShell scripts, SharePoint Framework, MobX, C#, Logic Apps, Playwright, Azure SQL Server, Graph API

• Project environment: Python, Java, Azure AI Studio & OpenAI Studio, embedding models, LLM as a judge
• Project language: German
• Project role(s): Project manager
• Project management for improving the performance of a chatbot
• Research and evaluation of approaches to improve and measure response accuracy and improve the chatbot's understanding of context
• Coordination of architecture decisions with the technical team and architects
• Coordination and transfer of research results into development tasks

• Setting up new desktop computers
• Setting up a VPS for automation, databases and chat interface
• Connecting to Azure AI Foundry
• Developing various n8n workflows for email, social media and document management
• Implementing a QA system (backups, error handling, HITL)
• Setting up and optimizing an inbound voice agent

• Digitization and automation of internal business processes with the Microsoft Power Platform: analysis, design, and development of end-to-end solutions to increase company-wide efficiency
• Creating user-friendly interfaces with Power Apps
• Automating workflows and approval processes with Power Automate
• Visualizing process KPIs with Power BI

• Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
• Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
• Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
• Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
• Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
• Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
• Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
• Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
• Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
• Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
• Integrated testing and CI/CD workflows for robust data pipeline deployment.