Automatic Speech Recognition Expert in Germany

• Introduction of 5 different process applications for different teams

• Release planning: scope and time management

• Resource/capacity planning

• Conducting Sprint Planning / retrospectives

• Increment planning (several sprints)

• Preparation of Steering Committee meetings / reporting to the Board

• Coordination / alignment with external suppliers / deliveries

• Resource planning for multiple projects

• Coordination with business department and development team

• Identification of best practices with IBM BAW

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

• Collection of KPIs using LogScale

• Analysis of application errors with LogScale / Queries

• User Story definitions / coordination of requirements with the business department and development team

• Testing and defect tracking

• UI/UX design of applications

• Preparation and moderation of Brown-Paper workshops

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

• Collection of team velocity / metrics

• Test execution

• Script for automated testing

• Test organization with business department and IT

• Recording and prioritization of defects

• Establishment and operation of the application

• Establishment of application monitoring with LogScale Dashboards

• Checking health endpoints with PowerShell

• Post Mortem analysis

• Establishment of Incident Management

• Establishment of Problem Management

• Analysis of errors using LogScale Queries and Dashboard

• Pre-processing of data for AI

• Evaluation using AI language models (Meta Llama 3.3 LLM and deepset Haystack) and RAG

• Installation of runtime environments for LLMs (Large Language Model)

• Evaluation of various LLMs

• Installation of RAG (Retrieval Augmented Generation) and integration with LLM

• Extraction of 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

• Responsible for the concept, planning, and delivery of product development for GetMyInvoices 2.0 and its subcomponent InvoiceRails
• Independently handled all aspects of the project, including concept development, writing ticket specifications, and coordinating developers
• Created, managed, and prioritized tickets to ensure all tasks were completed on time and to a high standard
• Conducted and/or coordinated tests and ensured the proper implementation of developed features and functionality
• Worked closely with developers to clarify technical requirements and ensure implementations met specifications
• Regularly reported on project progress and documented key decisions, changes, and risks
• Took the lead on all topics related to e-invoicing and Peppol, especially regarding the InvoiceRails component
• Provided internal guidance and shared knowledge about e-invoicing and Peppol with other teams and departments
• Tracked market trends and new developments in e-invoicing and Peppol to continuously adapt the product strategy
• Ensured the products’ long-term scalability and adaptability to future technological and legal changes in e-invoicing

• 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

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

Project management for 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 based on CRM and document data, voice-based capture and structuring of reports, detection and consolidation of duplicates in master data, and data-based market analyses. A key focus was a data-protection-compliant architecture that passed the internal IT security review and enabled production use.

• Translation of business requirements into clearly defined AI use cases with a focused product scope and clear value proposition
• Selection and evaluation of models and architecture options for text extraction, speech-to-text, and context enrichment from business systems, including LLM integration, function calling, and retrieval
• Development and implementation of an architecture with European hosting, data minimization, and masking of personal data as a prerequisite for approval
• Management of interfaces between business departments, IT, IT security, and the external development partner under restrictive data access conditions
• Coordination with the CIO and executive management levels on data access, risk assessment, and approval decisions
• Preparation for the transition to production use

Designed and developed an Azure-based AI and data platform transforming distributed project documents into searchable, structured knowledge for an enterprise AI assistant. Combined incremental document processing, hybrid keyword/vector retrieval, knowledge graph extraction, and conversational AI for Microsoft Teams and Microsoft 365 Copilot. Implemented Python document-processing pipelines for PDF, Word, Outlook and text files; Azure Cosmos DB lifecycle management; Azure AI Search; Azure AI Foundry and Azure OpenAI; identity-aware retrieval with Microsoft Entra ID; project authorization; a TypeScript Microsoft Agents SDK agent; Pulumi infrastructure; resilient scheduling, retry and diagnostic tooling. Technology stack: Python, TypeScript, FastAPI, Pydantic, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Cosmos DB, Blob/Table Storage, Container Apps/Jobs, Service Bus, Key Vault, Entra ID, Docker, Pulumi, pytest, Microsoft Teams and Microsoft 365 Copilot.

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.

• ▶ 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.

Independently designed and conducted on-site training for four employees without specific technical knowledge. Taught generative AI using concrete tasks from their professional everyday work.

• Topics: possible applications, practical use, review of AI results, data protection, and handling confidential information.
• Training materials prepared independently and content delivered in a way suited to the target group; written reference dated 14.05.2025 is available.

• Responsible for concept, camera, editing, animation, and grading for corporate and B2B productions
• Managing projects from pricing through shooting and post-production to delivery
• Since 2023, a continuous AI-supported production pipeline: Runway, Kling, Veo, Sora, and Seedance for image and moving image content
• ComfyUI for character consistency, ElevenLabs for voice, HeyGen for avatars
• Building reproducible workflows for scalable social media 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

• Lead a team of 15 annotators on large-scale computer vision projects for autonomous vehicle systems
• Developed comprehensive annotation guidelines that improved inter-annotator agreement by 35 percent
• Implemented quality control processes that reduced error rates by 42% across all projects
• Collaborated with ML engineers to identify edge cases and improve dataset quality
• Managed annotation projects for Fortune 500 clients, delivering 100% on time

• 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.

• Coach international tech professionals through career transitions, helping high achievers move from reactive decision-making to clarity-driven career and life choices.
• Combined software engineering and consulting background with coaching expertise to address challenges specific to the tech industry, including career stagnation, imposter syndrome, and uncertainty around AI's impact on the field.
• Some of my client's measurable outcomes include landing multiple concurrent interview processes after a multi-year career gap, launching independent product ventures, securing technical roles that improved both financial stability and job performance, transitioning into non-tech careers.
• ~200 hours of coaching sessions. Guided clients through 1:1 and group coaching engagements, ranging from single sessions to year-long partnerships, focused on sustainable, values-aligned career growth.

End-to-end responsibility for all products – conception, architecture, development, and production operations as the sole developer; also customer discussions, proposals, and marketing.

Underwriting Copilot – AI assistant for industrial insurance (in production with a customer)

• Supports underwriters in analyzing industrial insurance submissions – in production at an industrial insurer.
• Framework-independent RAG architecture with Hybrid Search (BM25 + pgvector) across large, heterogeneous document collections.
• 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 with a customer)

• Automatically collects and analyzes food 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.
• Complete CI/CD pipelines (GitHub Actions), production cloud deployment, interactive dashboards (Dash).

Kaiki GEO Atlas – GEO platform (in production with a customer)

• 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 additional LLM effort).

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 development)

BankingGPT – AI assistant for complaint management in cooperative banking

• Security architecture at its core: no AI draft reaches the customer without human approval – the approval decision is stored in auditable code, not in the language model (monotonic: the model may increase the level, but never lower it).
• Genuine agentic components, each with its own boundaries: the model selects tools itself through an MCP server (read-only, allowlisted, capped, fail-safe); sensitive cases are sent via the open A2A protocol (JSON-RPC, Agent Card, message/send/tasks/get; client implemented independently) to a standalone specialist agent (securities/law), which never lowers the review requirement (pinned by tests).
• Evaluation-driven across ten analysis rounds; an independent review and blind tests uncovered a security defect that nine automated runs had missed.
• Voice AI frontend, responding live: covered cases are answered during the conversation, while sensitive cases are escalated before generation; measured response latency < 7 s (local GPU STT/TTS).

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

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

• Genuinely agentic with LangGraph: ReAct agent with four tools and conversation memory – the model decides itself whether to use the manual (RAG, Chroma), a knowledge graph (GraphRAG, Neo4j/Cypher – decoding warning lights), or a workshop/booking service.
• Human-in-the-loop before the irreversible action: before every appointment booking, the graph pauses (interrupt) and obtains the driver's explicit confirmation – the same approval-before-action discipline as in BankingGPT, in a different framework.
• Eval as a CI gate: three-part scorecard (RAGAS grounding + deterministic tool-routing accuracy + DeepEval safety: does the answer mention the warning first in the case of 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.

• Development and rollout of a browser-based platform for camera streaming, augmented reality (AR), and visual computing
• Device-independent camera streaming for smartphones, tablets, desktop PCs, as well as VR and AR headsets
• Ensuring GDPR-compliant hosting in Open Telekom Cloud and other sovereign cloud providers
• Intuitive visual and collaborative features to increase efficiency and integrate smoothly into existing business software