Automatic Speech Recognition Experts in Germany

Built Python-based AI microservices and integrations for an AEC/VR Unity-based SaaS app, focusing on LLM/VLM capabilities, retrieval-backed systems, RESTful APIs, containerized deployment, and an automation microservice for the CAD-to-Unity pipeline.

• Developed a custom Hybrid A* based algorithm in C# to simulate hospital scenarios and detect early-stage design conflicts from collision/spatial data and generate structured reports.
• Solved and automated the time-consuming problem of converting CAD files to usable Unity environments with a custom-engineered and real-time pipeline using a ZeroMQ-based communication layer to distribute workloads across multiple processes and achieve real-time performance.
• Built a Dockerized FastAPI pipeline for CAD-to-Unity automation, combining vision-based object matching, image embeddings, and precomputed metadata to automatically map CAD objects to Unity behavior scripts, assign properties, and reduce repeated AI inference calls.
• Created documentation and examples to help technical users understand, configure, and extend the AI automation pipeline.

Project: Concept and implementation of an AI product for four business use cases

Project management of an AI project at a leading provider of large-scale catering and food services, where a production-ready AI product for four use cases was implemented together with an external development partner: automated briefings from CRM and document data, voice-based capture and structuring of reports, detection and merging of duplicates in master data, and data-based market analysis. A central focus was a privacy-compliant architecture that passed the internal IT security review and enabled productive use.

• Translating business requirements into clearly defined AI use cases with a clear product scope and clear value proposition
• Selecting and evaluating models and architecture options for text extraction, speech-to-text and context enrichment from business systems, including LLM integration, function calling and retrieval
• Designing and enforcing an architecture with European hosting, data minimization and masking of personal data as a prerequisite for approval
• Managing the interfaces between business, IT, IT security and the external development partner under restrictive data access conditions
• Coordinating with CIO and executive management on data access, risk assessment and approval decisions
• Preparing the transition into productive use

• Responsible for the concept, planning, and implementation of the product development of GetMyInvoices 2.0 and the subcomponent InvoiceRails
• Independent work on all aspects of the project, including concept, specification in tickets, and coordination of developers
• Creation, management, and prioritization of tickets to ensure all tasks are completed on time and with high quality
• Carrying out and/or coordinating tests and ensuring the proper implementation of the developed features and functionalities
• Close collaboration with developers to clarify technical requirements and ensure the implementations match the specifications
• Regular reporting on project progress and documentation of key decisions, changes, and risks
• Taking on the subject matter lead for all topics around e-invoicing and Peppol, especially in relation to the InvoiceRails component
• Internal consulting and knowledge sharing on e-invoicing and Peppol for other teams and departments
• Tracking market trends and new developments in e-invoicing and Peppol to continuously adapt the product strategy
• Ensuring the long-term scalability and flexibility of the products for future technical and regulatory changes in the e-invoicing area

• ▶ 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.

• Responsible for concept, camera, editing, animation, and grading for corporate and B2B productions
• Managing projects from budgeting to shoot and post-production through to delivery
• Since 2023, consistently using an AI-based production pipeline: Runway, Kling, Veo, Sora, and Seedance for stills and moving image
• ComfyUI for character consistency, ElevenLabs for voice, HeyGen for avatars
• Building reproducible workflows for scalable social formats
• Building local LLM infrastructure on my own GPU hardware: Ollama, multi-agent systems, RAG, speech-to-text, and text-to-speech
• Process automation for lead generation, email and API workflows, reporting, and document creation

• Industry: software manufacturer
• Tasks: project management; tracking and coordination of projects; stakeholder management; change management; prioritization of requirements; alignment of architecture; supplier management (internal and external); release management; monitoring defect resolution with the teams; AI-supported software development, software testing and code analysis; AI prompting, prompt engineering
• Software: Jira, Confluence, MS Project, MS Teams
• Environment: IT, software development, agile, artificial intelligence (AI)

• Further development of the existing SCCM 2509 implementation
• Schema extension, CAS extension
• Creating task sequences, in-place upgrade
• Patch management (WSUS)
• Security updates, feature updates
• Emergency fixes, application updates
• Incident, change, and problem management (3rd level)
• Active Directory, DNS, DHCP, GPMC
• Managing users, groups, OUs, computers
• Setting up GPOs
• Senior consultant for software packaging in an SCCM 2509 environment
• Project management ITIL standards
• Defect management
• SIT (Software Integration Test)
• SAT (Software Acceptance Test)
• UAT (User Acceptance Test)
• Adjusting Windows 11 25H2 client deployment
• Secunet SINA management systems administrator
• Secunet SINA Workstation 3.5.4
• Maintenance and configuration work in SINA management
• Importing and adjusting IPsec policies
• Retrieving and documenting status, firmware versions, and configurations of individual SINA devices
• Consulting and implementation in fault and incident management
• Implementation of documentation and rollout of new software versions
• Creating software packages based on PowerShell App Deployment Toolkit, Flexera AdminStudio for the W11 64-bit platform and Server 2025 / 2022
• Number of PC systems: approx. 1,850.

Label: PowerShell, VBS, Batch scripting

Label: SCCM 2503, Windows 11 64 Bit, Windows 2025 Server, Windows 2022 Server, Windows 2019 Server

• Teaching German literature and linguistics

• Built a Trend-Scouting AI system to automate technology intelligence in power electronics and semiconductors, combining Azure OpenAI with LangChain, Scrapy-based web crawling for structured, noise-free data acquisition, and automated PDF reporting for internal R&D use. Developed a FastAPI-based (Uvicorn) web application to validate LLM outputs, test prompt strategies, and enable interactive system evaluation.
• Developed a real-time STM32 binary telemetry debugger with a PyQt-based GUI, featuring header-based frame synchronization, anomaly detection, template-driven payload decoding, time-aligned buffering, and live signal visualization.
• Developed an AI-driven power inductor designer using surrogate regression models for accurate electromagnetic and thermal prediction. Integrated multi-objective NSGA-II optimization to generate efficient, manufacturable designs.

• Development of an AI-supported personality analysis based on the institute's SDWA4: online survey, AI-supported and automated evaluation, and email delivery
• Requirements analysis, prototype development, evaluation, derivation of a simplified SDWA4-light analysis, continuous improvement, design, Make automation, deployment

End-to-end responsibility for all products - concept, architecture, development, and production operation as the sole developer; in addition, customer meetings, proposals, and marketing.

Underwriting Copilot - AI assistant for industrial insurance (in production at customer sites)

• Supports underwriters in analyzing industrial insurance submissions - in production use at an industrial insurer.
• Framework-independent RAG architecture with Hybrid Search (BM25 + pgvector) across large, mixed document sets.
• Two-stage evaluation and observability pipeline (code assertions + LLM-as-Judge) that makes answer quality, retrieval accuracy, and citation integrity measurable in a regression-safe way.

Kaiki Menu Analyzer - Data intelligence platform (in production at customer sites)

• Automatically captures and analyzes menu data from around 25,000 German restaurants.
• Scalable 7-container architecture (FastAPI, partitioned PostgreSQL, Redis/RQ) with LLM-supported extraction of structured data from PDF, HTML, and images.
• Full CI/CD pipelines (GitHub Actions), production cloud deployment, interactive dashboards (Dash).

Kaiki GEO Atlas - GEO platform (in production at customer sites)

• Measures brand visibility across five AI engines (ChatGPT, Gemini, Perplexity, Grok, Claude), each augmented with web search, orchestrated as a DAG workflow pipeline (Dispatcher → Sub-workflows → Scoring → Report) with fail isolation.
• 6-container deployment (FastAPI, Celery, Redis, PostgreSQL); LLM cost estimation, PDF audit report, rule-based cross-signal insights (no extra LLM cost).

Data Pipeline & Analytics Platform - competitive analysis in the automotive aftermarket

• Automated data pipeline with gap analysis algorithms and role-based access control; 230+ tests.
• Backend with FastAPI, PostgreSQL, SQLAlchemy.

Product development (actively in progress)

BankingGPT - AI assistant for complaint management in cooperative banking

• Security architecture at the core: no AI draft reaches the customer without human approval - the approval decision is in auditable code, not in the language model (monotonic: the model may escalate, never downgrade).
• Real agentic building blocks, each with its own boundary: the model chooses tools itself through an MCP server (read-only, allowlist, capped, fail-safe); sensitive cases are handed off via an open A2A protocol (JSON-RPC, Agent Card, message/send/tasks/get; client implemented by me) to a separate specialist agent (securities/law), which never lowers the review requirement (pinned by test).
• Evaluation-driven over ten analysis rounds; uncovered a security flaw through independent review and blind tests that nine automated runs had missed.
• Voice AI frontend, responding live: covered cases are answered in the conversation, sensitive ones escalate before generation; response latency < 7 s measured (local GPU STT/TTS).

Stack & production readiness: Python, pydantic-ai, FastAPI/Celery, PostgreSQL/pgvector, FastMCP, fasta2a, Docker; multi-tenant capable (physical vector isolation per tenant), PII encrypted, OWASP-LLM reviewed, 275 tests, CI/CD; vendor-portable (Ollama / EU Cloud Vertex).

After-Sales Assistant - agentic RAG/GraphRAG assistant on public OEM manuals (automotive after-sales)

• Genuinely agentic on LangGraph: ReAct agent with four tools and conversation memory - the model decides on its own whether to use the manual (RAG, Chroma), a knowledge graph (GraphRAG, Neo4j/Cypher - decodes warning lights), or a workshop/booking service.
• Human-in-the-Loop before the irreversible action: before every appointment booking, the graph pauses (interrupt) and gets the driver's explicit confirmation - the same approval-before-action discipline as in BankingGPT, in a different framework.
• Eval as CI gate: a three-part scorecard (RAGAS grounding + deterministic tool-routing accuracy + DeepEval safety: does the answer mention the warning first when there is a critical warning?) blocks the pipeline; provider-agnostic (OpenAI/Azure/Anthropic), FastAPI with token streaming.

Stack: Python, LangChain/LangGraph, Chroma, Neo4j, RAGAS/DeepEval, FastAPI, Docker.

• Introducing 5 different process applications for various teams

• Release planning: scope and time management

• Resource/capacity planning

• Conducting sprint planning / retrospectives

• Increment planning (multiple sprints)

• Preparing steering committee meetings / reporting to the executive board

• Coordinating / aligning with external suppliers / deliveries

• Multi-project resource planning

• Aligning with the business unit and development team

• Identifying best practices with IBM BAW

• Cost control and planning for the project team and external service providers

• Collecting KPIs using LogScale

• Analyzing application errors with LogScale / queries

• Defining user stories / aligning requirements with the business unit and development team

• Testing and defect tracking

• UI/UX design of the application

• Preparing and facilitating brown-paper workshop

• Test concept, test data, test organization, test execution

• Recording team velocity / metrics

• Executing tests

• Scripts for automated testing

• Organizing tests with the business unit and IT

• Recording and prioritizing defects

• Setting up and operating the application

• Setting up application monitoring with LogScale dashboards

• Checking health endpoints with PowerShell

• Post mortem analysis

• Setting up incident management

• Setting up problem management

• Analyzing errors using LogScale queries and dashboard

• Pre-processing data for AI

• Conducting evaluation with AI language models (Meta Llama 3.3 LLM and deepset Haystack) and RAG

• Installing runtime environments for LLMs (large language model)

• Evaluating various LLMs

• Installing RAG (retrieval augmented generation) and integrating with LLM

• Extracting unstructured data with LLM and RAG

• Project based on IBM BAW (Business Automation Workflow), WebSphere Liberty, Domea, d.3, REST, LogScale (formerly Humio), Swagger, PowerShell, JIRA, Confluence, Lucom Interaction Platform (LIP), Mattermost, Jabber

• Optimized the data architecture (medallion) to better decouple processing stages and improve transparency and reproducibility
• Ensured technical quality of data processing in Databricks by introducing schema enforcement, data quality checks and a structured data architecture
• Orchestrated pipelines with Azure Data Factory
• Professionalized and automated the development and deployment process by integrating Git and GitHub Actions
• Led the Data Engineering team (3 members) in a functional role
• Conducted workshops to optimize and stabilize the data platform and the development process
• Collected and prioritized new requests, maintained the product backlog
• Technologies: Microsoft Azure (Data Lake, Data Factory), Databricks, Apache Spark (PySpark), Python, SQL, Git, Confluence, Power BI, Power Apps, Dataverse, MS SharePoint, Mural

• Consulting and execution of migration from Cisco AireOS to Cisco IOS-XE controller
• Solution design
• Configuration and staff training
• Products: Catalyst Center, Cisco controllers and IOS and COS access points, 9800-40, 5520, 8510, 9120, 9136, 9166, Catalyst switches, Aruba Clearpass
• Keywords: Tags, profiles, SSIDs, 802.1X authentication, captive portal, Identity PSK (iPSK), VLANs, troubleshooting, automation, RESTCONF, NETCONF, YANG