GPT Experts in Berlin

• 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

• Concept and implementation of data-heavy platforms for enterprise customers
• Design of complex AI-based interactions (chatbots, voice control, semantic search)
• Stakeholder management with more than 10 participants in cross-functional teams
• User research: interviews, usability tests, value validation
• Definition of quality metrics and execution of value analyses

Tools & methods: Figma, Adobe XD, Miro, Chat GPT, Claude | Scrum, Kanban, SAFe, Lean UX

Selected clients: VW Group, Volkswagen, BMW, Cariad, ABUS, Gieseke & Devrient, Hamburg Senate Chancellery, German Red Cross, Deutsche Bahn, Immoscout24, Relynk

• Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
• Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
• Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
• Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
• Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
• Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
• Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.

AI Platform Backend – Senior Developer

Brought in to design and build a backend for an AI platform from scratch, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.

Tasks and responsibilities

• Architected and implemented a multi-LLM orchestration layer with Semantic Kernel to integrate GPT-4 and other providers for core platform logic and AI influencer personas, reducing model-switching overhead by abstracting provider APIs behind a single interface.
• Designed and developed a backend from scratch in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure setup on Azure.
• Built a real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for live operation of AI influencer personas at scale.
• Developed a media management service with integration of cloud object storage for upload and retrieval of influencer-generated content.
• Created an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing the production error rate.

Tools and technologies: C#, .NET, ASP.NET Core, Python, TypeScript, MySQL, Semantic Kernel, EF Core, Minimal APIs, LLM Orchestration, Prompt Engineering, Agentic AI, Generative AI, AI-Assisted Engineering, Claude Code, GitHub Copilot, Google Gemini, OpenAI API, Ollama, Redis, Azure, Azure Container Apps, Azure Database for MySQL, Docker, GitHub Actions, Clean Architecture, Vertical Slice Architecture, CQRS, Domain-Driven Design, REST API, xUnit, Integration Testing, Unit Testing, Jira, Confluence, Scrum

• Built a JTBD-based prioritization framework for 3,000+ accumulated feature requests, identified 27 broker jobs, validated 8 through 25 user interviews, and used the resulting job map as a live prioritization filter for all incoming channels (Upvoty, CSAT, consulting tickets).
• Responsible for the Scout24 Lighthouse initiative: Document Intelligence with full RAG architecture (semantic chunking, bge-m3 embeddings, pgvector, BM25+Dense hybrid retrieval).
• Reduced lead time of customer feature requests to 3.1 days through code analysis, ticket specification, and independent implementation using a coding agent (Codex).
• Developed an LLM-based support agent (GPT-4o mini, Codex-generated merge requests) that reduced 3rd-level escalations from 40% to 5% of all monthly tickets.
• Integrated six partners through technical coordination, specification, backlog and release management, and led seven full stack developers.
• Eliminated regulatory exposure for brokers in six weeks through risk analysis (BGH ruling on distance selling/GDPR), new audit features, and coordination with legal and data protection officers.

• Introduction to AI and Automation
• The Automation Project
• Make.com Foundations
• Automation Basics: Data Management
• Document Workflows, Troubleshoot, and Automate Reports
• Digression: Data as the Foundation of AI Systems
• Implementing AI Solutions in Practice
• Digression: Large Language Models (LLMs)

• 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.

• Full automation of the software development process: ticket analysis → AI coding agents → pull request → automated code review → deployment
• Multi-tenant architecture with 82 database models and real-time WebSocket monitoring
• Integration of 40+ AI tools with Claude & GPT
• Tech stack: React, TypeScript, Express.js, PostgreSQL, Redis, BullMQ
• Platform in productive use with paying customers

• Strategic and operational management: strategy, marketing, communications, sponsorship, lead management, growth campaigns as well as customer success, partly AI-supported

• Setup and process design in CRM and customer journey

• Design and implementation of proactive targeting strategies as well as innovative marketing initiatives based on market and KPI analyses

• Stakeholder management with internal/external interfaces and agencies

• Steering and monitoring of SEO/SEA campaigns as well as website relaunches, email automation, PR and content campaigns as well as various performance marketing and social media campaigns

• Creation of the product value proposition as well as further development of the UX/UI of website and print materials

• Selection, leadership and development of teams (up to 17 staff)

• Participation in conferences and trade fairs as speaker and moderator

• Tripled lead numbers through digitization of marketing and sales processes

• Significant increase in profitability through optimization of the customer journey and marketing automation

• New customer acquisition and business development through product extension and funnel optimization

• Conducting cybersecurity readiness checks based on an in-house assessment methodology
• Analyzing the external attack surface using the Graydaxe EASM platform
• Assessing maturity levels and deriving prioritized recommendations for action

• Overhaul of the entire design and implementation of new UI/UX aspects using React
• Planning of the software architecture and structuring of the project for long-term scalability using Git for version control

• Defined product vision, roadmap, and subscription-based monetization model.
• Architected multimodel AI orchestration (Gemini + GPT fallback) ensuring reliability and cost efficiency.
• Designed explainable insight engine with confidence scoring and agile antipattern detection.
• Built and deployed full-stack architecture (FastAPI, PostgreSQL, React) with secure authentication and quota governance.
• Tech: Python, FastAPI, PostgreSQL, React, TypeScript, Stripe, Gemini API, OpenAI API.

• Establishing Identity & Directory Management as a new (split-off) team & product within the IAM cluster.
• Leading the “Identity & Directory Management” product (8 people) as Product Owner.
• Concept for ‘Digital Identities’, i.e. IDs with n users/accounts and strategy for a modernized product offering.
• Upgrading APIs, migrating to a containerized infrastructure, rolling out international markets & standardized solutions across the REWE enterprise group.
• Tech: OpenText™ (NetIQ) eDirectory & Identity Manager, LDAP, SAP HR/HCM, Docker/Kubernetes/Podman, REST APIs, Microsoft Active Directory & Entra ID, postgresDB, Keycloak, Ansible, Cyberark (PAM), Apache Kafka, Jira, Confluence, Miro.