Vision-Language Model Experts in Germany

Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.

Selected client engagement: Scalutions

• Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.

• Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.

• Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.

• Developed and implemented AI-based perception and multimodal systems for real-world environments
• Built, trained, and evaluated Machine Learning and Deep Learning models using Python, PyTorch, TensorFlow, and OpenCV
• Worked with Vision-Language Models (VLMs), Large Language Models (LLMs), transformer-based architectures, and multimodal AI systems
• Applied LoRA-based fine-tuning techniques and experimented with diffusion models for generative and multimodal AI applications
• Developed multimodal perception pipelines using camera, LiDAR, and sensor data
• Designed end-to-end workflows for data processing, model training, evaluation, benchmarking, and robustness analysis
• Utilized HuggingFace Transformers and modern Deep Learning frameworks for AI experimentation and deployment workflows
• Applied GPU-accelerated computing, CUDA-based processing, ONNX, and TensorRT optimization for efficient inference and large-scale model training
• Collaborated with industry partners including Valeo and REHAU on applied AI and intelligent system projects
• Developed scalable AI architectures and prototype software solutions for automation and perception tasks

• Automatic detection of content on various documents
• Recommendation Engine
• Dynamic Pricing

• Architect end-to-end AI/ML solutions, with a strong focus on advanced LLM applications, autonomous agentic AI workflows using MCP toolset, vLM embedding computations, and robust agentic RAG pipelines to drive real-world business impact for clients across diverse sectors.
• Designed intelligent agentic AI workflows to automate knowledge retrieval and reasoning for B2B clients, accelerating business impact.

• Embedded software development for EMS safety boards in medical technology according to IEC 62304 / ISO 13485
• Technologies: C++, OpenCV, Python, Qt6, JTAG, UART, CMake, IEC 62304, ISO 13485

• Insurance service provider*

Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.

Implementation:

• Architecture and production implementation of an on-premise OCR solution with full data ownership
• Methods for recognizing document structures as the basis for automated further processing
• ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations

Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year

Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL

• Developed an evaluation framework for AI-generated multiview driving videos intended for perception and embodied-AI/VLA-related training workflows.
• Designed automated checks for temporal coherence, cross-camera consistency, semantic correctness, and multiview geometric quality, exposing failure modes relevant to autonomous systems.
• Combined classical computer vision, learned visual representations, and vision-language models to convert complex video artifacts into measurable engineering signals.
• Built repeatable benchmarking and failure-analysis workflows to support model comparison, data-quality decisions, and system-improvement discussions.

• Developing software tools within the EICACS program (LDACS project) supporting secure avionics communication.
• Built Python-based automation and monitoring services to validate AI components under Trustable AI guidelines.
• Designed CI/CD and test pipelines improving reproducibility and reliability across teams.

• Designed and built a multi-tenant Python/Flask API platform with JWT + API-key authentication, scoped access control, and service-level orchestration as the backbone for AI applications.
• Built a multimodal RAG system with hybrid chunking, dense/sparse embeddings, hybrid retrieval, reranking, and vector search to deliver grounded, high-precision responses across enterprise data.
• Productionized AI workflows with Docker, CI/CD, Redis-backed async job tracking, webhook callbacks, external AI/media service integrations, and runtime health/reliability controls.

• Evaluation and analysis of camera-based traffic light and sign recognition system on various LLM-based autonomous driving systems (LMDrive, BEVDriver)
• Implemented VLM based traffic notice instruction generation unit for closed-loop autonomous driving system which alerts driver in unforeseen driving incidents
• Developed independent LLM-based local chatbot with Llama, DeepSeek and Qwen including MLflow evaluation framework

• Design and implement an end-to-end Siemens NX based pipeline to convert OBJ CAD models into graph representations by applying AI-driven clustering of mesh faces into nodes and face adjacency for edges, streamlining GNN integration
• Generate a large-scale synthetic 3D CAD dataset, annotating parts with few MFCAD-style features to ensure balanced, diverse training data for GNN workflows
• Support the design, training, and evaluation of graph neural network architectures for AI-driven detection and classification of geometric features in 3D CAD shapes, accelerating feature-recognition workflows

• Developed a synthetic data generation framework using 3D simulation (NVIDIA Omniverse) and Generative AI (Stable Diffusion) to model and augment industrial surface defects.
• Trained and evaluated Computer Vision models (YOLO, DETR), achieving 94% detection accuracy on real-world samples and demonstrating successful simulation-to-reality transfer.
• Applied domain adaptation to improve simulation-to-reality transfer, enabling scalable Industrial AI for automated quality inspection and reducing manufacturing downtime.

• Developed an end-to-end AI pipeline for analyzing 2D exploded-view drawings using computer vision and deep learning models.
• Integrated YOLO-based object detection (Bounding Boxes, Post-Processing, Overlap Handling) for accurate part and callout detection.
• Applied the Segment Anything Model (SAM) for fine-grained segmentation and separation of individual components.
• Implemented OCR and feature extraction modules, and compared Vision Language Models (VLM) and traditional computer vision approaches in terms of accuracy, runtime, and scalability.

• Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
• Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
• Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.