Rate Limiting Experts in Germany

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

• Provided technical leadership for a 4-engineer team delivering 3 major client platforms in 12 months with microservices architecture and scalability solutions — 100% of scoped majors shipped ahead of schedule vs. planned milestones (baseline: prior releases often slipped 1–2 sprints).
• Ran AI model evaluation and model outputs evaluation on LLM/AI vendor APIs: safety, completeness, instruction adherence, and groundedness review before go-live; cut escaped bad outputs in AI-integrated release checklists from recurring UAT findings to near-zero on final promote.
• Drove API development and performance optimization for payment, exchange, and AI services; fail-closed error handling and payload validation reduced integration rework cycles by ~35% vs. the first AI integration pass.
• Applied software testing, testing frameworks, code quality assurance, and code refactoring with continuous integration gates; first-pass PR acceptance improved across the team and production hotfixes on AI adapters dropped noticeably after review standards landed.
• Owned DevOps practices: Docker, GitHub Actions, Jenkins-compatible pipelines, and version control workflows — cut deployment time ~50% vs. pre-automation baseline and stabilized releases across 3 client environments.
• Implemented verifier/oracle-style pass-fail checks in container sandboxes (Harbor/Terminal-Bench aligned); wrote technical documentation so failures cleared in one review cycle.
• Led cross-functional collaboration with product and client stakeholders; translated AI evaluation scores and risk findings into plain-language briefs for non-technical partners, unblocking go/no-go decisions without extra engineering meetings.
• Used agile methodologies for sprint planning and backlog ownership; mentored engineers so mid-level contributors owned AI adapter modules independently by mid-engagement.

• ▶ 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.

• Devised a new Domain-Driven Design architecture for a core system: reverse-engineered a central component, refactored the data-access layer to minimise database round-trips (improving scalability) and migrated processing to async.
• Decomposed the platform into independent .NET Core microservices (database-per-service) with RabbitMQ pub/sub using the Outbox Pattern + Saga choreography, behind an Ocelot API Gateway (JWT, rate limiting, CORS, health checks).
• Delivered on .NET Core / Angular / SQL Server / EF Core with Docker and Azure DevOps CI/CD; implemented health checks and CORS; contributes technical designs for stories in agile Scrum.
• Sole ADR owner; mentored 3 engineers and presented architecture decisions directly to the Director of Corporate IT.

• Technical program ownership for the IPv6 migration at DRV RP and DRV BW, with a focus on migration planning, execution structure, and cross-functional technical coordination.
• Designed and implemented an operational control model with dashboard, action board, KPI portfolio, risk register, and decision index to translate technical topics into structured delivery artifacts.
• Coordinated technical groundwork for architecture and rollout across IPv6 addressing, segmentation, dual-stack target design, test-lab planning, and cross-team dependencies.
• Supported security and compliance-related requirements in the context of BSI, NIS2, and critical infrastructure, translating them into traceable evidence, risks, and management reporting.
• Achievement: Established a reusable intake-to-governance workflow for systematically capturing technical actions, risks, open issues, and evidence requirements.
• Achievement: Created an operational baseline for technical program execution with measurable KPIs, clear ownership, and transparent decision support.

• Presentation introducing generic AI and large language models
• Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
• Systematic review of AI tools along the SDLC and holistic systems
• Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
• Facilitated the discussion and derived next steps for introducing AI development tools

• Built and operated an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for automated deployment, monitoring and scaling of forecasting models (e.g. Temporal Fusion Transformer, Informer, Autoformer)
• Implemented CI/CD pipelines in Azure DevOps for the complete ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA clusters) through training and evaluation to model registry and endpoint deployment
• Integrated MLflow for experiment tracking, model versioning, performance monitoring and automated registration in Azure Model Registry
• Developed and containerized PyTorch training jobs with CUDA (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0
• Set up monitoring and alerting mechanisms (Prometheus, MLflow metrics), centralized logging and cost tracking
• Configured security (OAuth2) and rate limiting via APIM
• Automated infrastructure provisioning and model deployment using Terraform, Helm and Azure CLI; integrated with existing market data systems and event pipelines
• Migrated existing workloads and databases (IONOS → Azure, MongoDB) integrating them into central MLOps workflows and internal networks
• Extended the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports
• Analyzed and designed a software solution to efficiently process high-volume data (>3000 messages/sec) (market data store)
• Developed Spring Boot / Java 21 containers with RabbitMQ to distribute market data via MongoDB (Kubernetes) with fast data storage in Redis RMaps, deduplication, forwarding of messages to read model queues and building read models for UI display in MongoDB
• Integrated RESTHeart to generate a REST API for MongoDB
• Migrated to MongoDB ClickHouse for mass ingests and automatic deduplication using the ReplicatedMergeTree engine in ClickHouse
• Built a Python Apache Arrow Flight service for querying the ClickHouse DB in milliseconds for complex queries (gRPC protocol / ClickHouse column-based queries)
• Developed an Angular frontend to simplify data queries and master data maintenance
• Used agentic coding with remote and local LLMs (Claude, Ollama Qwen, OpenLLM) and MCP servers
• Created Python scripts for transforming and cleaning incoming market data (Pandas, scikit-learn)

• Developed a Proof of Concept for distributed tracing using OpenTelemetry within a microservices architecture, enhancing system observability and reducing mean-time-to-resolution for issues.
• Built and maintained backend services using Java and Spring Boot, ensuring high reliability and performance for payment transaction workflows.
• Implemented comprehensive automated testing with JUnit and Mockito, achieving 95% test coverage to ensure code quality and maintainability.
• Gained insight into payment transaction workflows, validating end-to-end system reliability and gaining domain knowledge in financial processes.
• Tech used: Java, Spring Boot, Microservices, REST API, Kafka, Grafana, Datadog, Docker, MongoDB

• Developed a multi-agent system connecting LangChain ZeroShotAgent with custom tools for live APIs and task automation.
• Built a FastAPI backend for Jira ticket creation, triage and assignment, auto classification of severity, deduplication, SLA setup, on-call rotation, bidirectional sync of status and comments.
• Added Slack alerts and RAG knowledge lookup with FAISS or pgvector to suggest fixes, optional PagerDuty escalation on policy breaches.
• Orchestrated agents with a router and a Celery plus Redis queue, retries with backoff, rate limits, idempotency keys, human in the loop approvals.
• Implemented guardrails and observability, prompt versioning, token and cost budgets, PII redaction, tool-use allowlists, timeouts, OpenTelemetry tracing, dashboards for accuracy and latency, deployed on Kubernetes with feature flags and canary rollouts.

• Advanced applied cybersecurity training focused on offensive and defensive operations.
• Designed and executed multi-vector penetration campaigns, including enumeration, privilege escalation, and lateral movement simulations across hybrid networks.
• Implemented SOC automation pipelines for incident response using Python, Bash, and SIEM integration.
• Conducted digital forensics and threat intelligence operations (malware reverse analysis, PCAP correlation, IDS/IPS rule engineering).
• Specialized in adaptive adversary emulation, Blue-Team countermeasures, and MITRE ATT&CK–based detection strategy.