llama.cpp Experts in Germany

• Developing a professional scheduler for organizations with specific detailed requirements
• Evaluating different existing software solutions
• Creating a list of technical requirements
• Implementing these requirements
• Selected technologies: WebDAV, CalDAV, Rust, Baikal, OAuth, Keycloak

AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.

• 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

• Design, preparation and delivery of a webinar on 'RAG in Practice: How publishers create real value with AI'
• Preparing technical and strategic content on Retrieval Augmented Generation (RAG) for a mixed audience from the publishing industry
• Presenting specific use cases, technical backgrounds, common challenges and solution approaches when using RAG
• Providing practical insights into data preparation, model selection and output optimization in the context of digital publishing portals
• Conceptual and technical preparation of the webinar
• Selecting and presenting practical use cases from the publishing environment
• Developing technical backgrounds for implementing RAG systems
• Presenting and explaining typical challenges and solution strategies
• Large Language Models (LLMs)
• Retrieval Augmented Generation (RAG)

• Vram.run: Rust, TypeScript, HF Inference API with 19 providers, 220+ HW configs, and 30+ cloud GPUs. Search a model to see which API providers serve it, which GPUs can run it locally (and how fast), and what cloud rental would cost. Or search your hardware and see what fits. Also includes a Rust CLI.
• Psychotron: JavaScript, Web Audio API, AudioWorklet, Canvas 2D. Front-end for flash fiction audiobook with Web Audio DSP chain featuring pitch-shifting, 12-voice chorus, flanger, 13-band EQ, and convolver reverb. Includes a 2D canvas effect morphing engine and synchronized teleprompter.
• RecentWork: Swift, macOS, FSEvents, launchd. macOS daemon that watches project directories and maintains a flat folder of symlinks to recently modified files. Homebrew installable.
• Mini-llm: Bash, macOS, launchd, Ollama, llama.cpp, MLX, Open WebUI. Single command that turns a Mac Mini into a headless AI server.
• ThatSlop: JavaScript. Chrome/Firefox extension for AI content detection on LinkedIn and Twitter.
• Smux: Bash, tmux. Human-friendly tmux wrapper that is Homebrew installable.
• Learn Rust Course: Rust. Course on Rust’s memory model for C++ programmers, written from experience of transitioning from C++ to Rust at Irreducible.

• Further training in Snowflake and Google Looker
• Working with LLMs: local models (Llama, Mistral, Gemma, Phi, Qwen, DeepSeek, Bitnet, Flux, Whisper), OpenAI API, frontends (ollama, openwebui, loacalai)
• Inference methods: llama.cpp, vLLM, transformer
• Quantization, benchmarking, prompting
• LLM Agents (Tool/Function Calling, LangChain, LangGraph, MCP)
• Topics: attention, reasoning, chain of thoughts, RAG, GraphRAG, mlflow
• Cloud hosted: ChatGPT, Claude, Gemini

• Software development of a B2B SaaS for AI-based search in internal candidate pools of recruitment agencies
• Design of a multi-tenant, hybrid architecture with dedicated GPU servers and secure cloud integration
• AI-Engineering
• LLMOps
• Python
• FastAPI

• Deployed and optimized Vision-Language-Action (VLA) and diffusion policy models on NVIDIA Jetson Orin and Jetson Thor, meeting real-time inference latency targets for humanoid robot control loops.
• Built TensorRT engine pipelines (PyTorch → ONNX → TensorRT) with INT8/FP8 post-training quantization, calibration dataset design, and quantization-aware validation, reducing inference memory footprint by over 3× on Jetson without accuracy regression.
• Developed custom CUDA C++ plugins and CUDA Graphs for latency-deterministic, real-time policy execution – meeting hard runtime and memory constraints on embedded GPU targets.
• Developed an inference engine for VLA models on top of llama.cpp bringing different VLA policies under single runtime, packaging each as a single self-contained GGUF that needs no Python or PyTorch.
• Profiled and tuned GPU execution using NVIDIA Nsight Systems and Nsight Compute, identifying CUDA kernel bottlenecks, memory bandwidth saturation, and SM occupancy issues across Jetson Orin and Thor compute profiles for cross-layer performance optimization.