Freelancer profile translated to English.
Back to original languageAbout Maximilian
I develop and implement production-ready AI systems – from development to (edge) deployment.
Focus areas:
- Real-time Object Detection & Tracking
- Other Computer Vision tasks (e.g., Segmentation, Depth Estimation, OCR, and Image Retrieval, etc.)
- Voice Agents, LLM adaptation and integration, Document Retrieval
- Model optimization & quantization for fast runtimes (NVIDIA Jetson, TensorRT, ONNX)
- MLOps & scalable training pipelines (Kubernetes, MLflow, multi-GPU)
- Implementation of AI web applications and APIs (e.g., also Voice Agents, tooling platforms, and much more).
With over 8 years of experience in developing safety-critical AI systems and leading the complete 2D object detection & tracking stack in my department at Hensoldt, I develop robust, production-ready computer vision solutions.
I seamlessly integrate state-of-the-art AI modules (object detection, segmentation, depth estimation, OCR, motion detection, LLMs, voice systems, and much more) into all types of systems – from edge devices to cloud infrastructures, web applications, mobile apps, and existing enterprise software. Via REST APIs, scalable backend architectures, and edge-to-cloud pipelines.
Result: Performant, maintainable, and immediately deployable AI systems – fast, pragmatic, and at the highest technical level.
Currently available part-time (approx. 8-10 hours/week) for:
Consulting & architecture reviews
Model training & optimization
Prototypes to production
Implementation of an AI (web) app
Languages: German (native speaker), English (fluent).
German
Native or bilingual
English
Fluent
Remote only
Primarily works remotely
Experience
- HensoldtFull-time professionalOctober 2021 - Today (4 years and 8 months)Munich, Germany
- Take lead and ownership for the development of the complete 2D object detection and tracking software stack, from research to deployment
- Manage and curate internal training datasets, including defining annotation strategies and overseeing labeling workflows
- Implement cutting-edge real-time object detection architectures and adapt them to production scenarios
- Design and implement customized, problem-specific deep learning algorithms for various computer vision tasks on edge devices like NVIDIA Jetson using C++ and C
- Key role in designing, deploying, and maintaining a multi-node, GPU-accelerated Ku bernetes cluster, ensuring it was fully optimized and user-friendly for machine-learning pipelines
- Develop a large-scale deep learning training pipeline using modern MLOps practices (Kubernetes, multi-server training, MLFlow) to train competitive, task-specific computer vision models on large datasets
- Built and deployed motion-based algorithms for the detection of tiny moving objects, specializing in drone identification
- Quantize deep learning models to optimize inference speed for edge deployment (not object-specific) to accelerate the labeling process
- Design and implement an image retrieval system based on feature-space similarity
- Train and deploy high-performance panoptic segmentation models with strict latency constraints
- Train and deploy depth estimation models optimized for real-time inference
- Implement an OCR system supporting multiple languages efficiently
- Design and operate scalable model-serving infrastructure using Kubernetes Pods and Triton Inference Server, delivering multiple ML models via REST APIs to customer-facing OSINT applications
- Regularly review state-of-the-art research to inform architectural and strategic decisions
- Develop a semi-supervised deep learning model for radar-based object identification
- Define tasks for, interview and supervise Master students, ensuring alignment with team objectives and research quality
- Fraunhofer IKSFull-time professionalMarch 2019 - September 2021 (2 years and 6 months)Munich, Germany
- Identifying, extenting and integrating promising methods for improving safety, explainabil ity and reliability of autonomous systems which are utilizing Machine Learning methods
- Reproduction and evaluation of state-of-the-art methods for quantifying uncertainty and detecting out-of-distribution examples in Deep Neural Networks applied on Computer Vision problems (Classification, Segmentation, Keypoint Estimation, 2D/3D Object Detection)
- Working in compliance with functional safety standards such as SOTIF(ISO/PAS 21448) Development of new methods for uncertainty quantification in 2D and 3D Object Detection
- Integrating developed methods into demonstrators and proof-of-concept prototypes
- Publication and presentation of scientific papers
- Supporting business development on customer acquisition from the industry
- Communicating research with different departments including marketing
- Participation in interviewing potential new employees for the department
- Supervising working students and utilizing their contributions
- Participation in events and meetings for exchanging ideas with different research institutes and industry
- Leading a research project end-to-end, providing strategic direction, coordinating tasks of team members, and ensuring delivery of high-quality, technically sound outcomes
- Developed and authored competitive research proposals and funding applications, trans lating complex AI concepts into clear, compelling narratives aligned with sponsor priorities
- Managed project operations and administrative processes including budgeting, reporting, compliance, and stakeholder communication to ensure smooth execution and sustained project momentum
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Education
- BSc Engineering ScienceTechnical University of Munich2017BSc Engineering Science
- MSc Artificial IntelligenceThe University of Edinburgh2018MSc Artificial Intelligence