Senior AI/ML Engineer & Applied Scientist | Computer Vision, LLM/RAG, Medical AI, MLOps & AI Governance
Aktualisiert am 17.06.2026
Profil
Freiberufler / Selbstständiger
Remote-Arbeit
Verfügbar ab: 01.07.2026
Verfügbar zu: 100%
davon vor Ort: 100%
Künstliche Intelligenz
Machine Learning
Bildverarbeitung
Deep Learning
Deep Neural Network
Python
PyTorch
Large Language Models
RAG
Multi-Agent Systems
Natural Language Processing
Computer Vision
Medical Image Processing
Vision Transformers
AI Governance
ISO/IEC 42001
LangChain
MLOps
Docker
Object Detection
Image Segmentation
AI Management System
Turkish
native
English
C1, fluent in scientific and technical communication
German
B2?C1, actively improving

Einsatzorte

Einsatzorte

Heidelberg (+75km) Munich (+75km)
Deutschland, Schweiz, Österreich
möglich

Projekte

Projekte

2 years
2024-04 - 2026-03

Developed reproducible Python pipelines

Research Associate, Medical Computer Vision
Research Associate, Medical Computer Vision
  • Developed reproducible Python pipelines for high-throughput Computational Pathology and spatial transcriptomics (Visium HD), including cell segmentation, cell-type annotation, and gene-expression prediction in containerized workflows.
  • Built aRAG/LLMpipelinetostandardizeapproximately16,000free-text surgical descriptions to SNOMED CT; designed the data integration layer and evaluated outputs for correctness and practical usability.
  • Developed and evaluated foundation models for surgical video understanding using Vision Transformers, self-supervised learning, and Vision-Language approaches.
  • Workedinaninterdisciplinary medical AI environment with requirements from research, clinical workflows, data integration, validation, documentation, and reproducibility.
German Cancer Research Center (DKFZ), Heidelberg
2 years 7 months
2021-09 - 2024-03

AI project on facial phenotyping

Postdoctoral Researcher, Human-Centered Artificial Intelligence
Postdoctoral Researcher, Human-Centered Artificial Intelligence
  • Led an AI project on facial phenotyping for rare genetic disorders; improved clinical experts? diagnostic accuracy by 20%, built a clinical decision-support prototype, and coordinated validation with the National Institutes of Health (NIH, USA).
  • Planned research milestones, coordinated interdisciplinary collaboration, and translated AI research results into a clinically relevant prototype.
  • Conducted additional research on 3D face shape modeling for emotion and action-unit recognition.
University of Augsburg, Augsburg
3 years 8 months
2017-08 - 2021-03

Developed AI models

Doctoral Researcher, Learning and Behavior Analysis
Doctoral Researcher, Learning and Behavior Analysis
  • Developed AI models for automated student engagement estimation using multimodal real-world classroom data; published and presented results at international venues.
  • Designed and built a multi-camera IP network for real-time audiovisual data acquisition; led data collection at partner schools, drafted the ethics application, and implemented data-protection requirements.
  • Gained practical experience in multimodal sensing, video analysis, experimental design, data protection, and real-world AI validation.
University of Tübingen, Tübingen
6 months
2019-01 - 2019-06

Conducted research on deep graph neural networks and equivariance properties

Visiting Researcher
Visiting Researcher
Brown University / ICERM, Providence, Rhode Island, USA
2 years 7 months
2015-01 - 2017-07

Developed self-supervised learning methods

Research Associate, Deep Learning
Research Associate, Deep Learning
  • Developed self-supervised learning methods for human pose estimation and video retrieval.
  • Built pedestrian detection models for vehicle safety in collaboration with Robert Bosch GmbH, connecting deep learning research with industrial computer vision use cases.
Heidelberg University, Heidelberg
3 years 4 months
2010-09 - 2013-12

Operated and maintained electronic command

Technical Officer, Electronics
Technical Officer, Electronics
  • Operated and maintained electronic command, control, communication, and sensor systems on a frigate and a fast patrol boat.
  • Served as watch officer with responsibility for technical readiness, reliability, and operational discipline in mission-critical environments.
Turkish Naval Forces, Turkey

Aus- und Weiterbildung

Aus- und Weiterbildung

3 years 7 months
2017-08 - 2021-02

Ph.D. in Computer Science (magna cum laude)

University of Tübingen, Tübingen
University of Tübingen, Tübingen
2 years 2 months
2012-08 - 2014-09

M.Sc. Electronics Engineering

Istanbul Technical University, Istanbul, Turkey
Istanbul Technical University, Istanbul, Turkey
4 years 2 months
2006-07 - 2010-08

B.Sc. Electronics Engineering (Control Systems)

Turkish Naval Academy? National Defense University, Istanbul, Turkey
Turkish Naval Academy? National Defense University, Istanbul, Turkey

Kompetenzen

Kompetenzen

Top-Skills

Künstliche Intelligenz Machine Learning Bildverarbeitung Deep Learning Deep Neural Network Python PyTorch Large Language Models RAG Multi-Agent Systems Natural Language Processing Computer Vision Medical Image Processing Vision Transformers AI Governance ISO/IEC 42001 LangChain MLOps Docker Object Detection Image Segmentation AI Management System

Produkte / Standards / Erfahrungen / Methoden

Profile

  • Senior AI/ML Engineer and Applied Scientist with a Ph.D. in Computer Science, an engineering background in electronics, and 10+ years of hands-on experience in applied machine learning, deep learning, Computer Vision, medical AI, LLM/RAG systems, and data-intensive AI pipelines.
  • Experienced in delivering AI solutions from requirements analysis, feasibility assessment, data and model design, implementation, training, evaluation, and validation to documentation, reproducible workflows, and deployment-oriented handover.
  • Strong practical background in Python, PyTorch, deeplearning, VisionTransformers, self-supervised learning, Lang Chain/LangGraph, LlamaIndex, Docker, MLflow, cloud-based ML workflows, and interdisciplinary AI projects with clinical, scientific, and industrial partners.
  • TÜV-certified AI Officer (KI-Beauftragter) with knowledge of EUAI Act, ISO/IEC42001, GDPR, risk classification, audit trails, AI documentation, and governance requirements for trustworthy and regulated AI systems.


Freelance Project Focus

  • Preferred roles: AI/ML Engineer, Machine Learning Engineer, Computer Vision Engineer, LLM/RAG Engineer, MLOps Engineer, Data Scientist, Applied AI Consultant, AI Governance Consultant, KI-Beauftragter.
  • Project types: AI proof-of-concepts, production-oriented ML prototypes, LLM/RAG systems, computer vision pipelines, medical and scientific AI applications, model evaluation, MLOps workflows, AI governance documentation, and EU AI Act readiness.
  • Availability: immediately; remote, hybrid, or on-site in Germany by agreement


Technical Skills

  • AI/ML & Computer Vision: PyTorch, deep learning, Computer Vision, Vision Transformers, selfsupervised learning, vision-language models, medical image analysis, surgical video analysis, computational pathology, human pose estimation, pedestrian detection.
  • LLM, NLP &Agentic Systems: Large Language Models, Retrieval-Augmented Generation (RAG), LangChain, LangGraph, LlamaIndex, OpenAI and Anthropic APIs, Codex and Claude Code, SNOMEDCT standardization, multi-agent systems.
  • Programming & Data Engineering: Python (OOP, Clean Code), C/C++, CUDA, Bash, Git, CI/CD, testing, REST APIs, HDF5, PostgreSQL, MongoDB, data-intensive pipeline development.
  • MLOps, Cloud & Reproducibility: Docker, MLflow, Weights&Biases, AWS, AzureML, multi-node training, containerized workflows, experiment tracking, reproducible AI pipelines.
  • AI Governance & Compliance: EUAIAct, ISO/IEC42001, GDPR, riskclassification, AI documentation, validation, audit trails, governance processes for high-risk and regulated AI systems.


Selected Project Themes

  • LLM/RAG for medical text standardization: RAG pipeline for mapping approximately 16,000 free text surgical descriptions to SNOMED CT, including data integration and output evaluation.
  • Medical Computer Vision and Computational Pathology: AI pipelines for spatial transcriptomics, cell segmentation, cell-type annotation, and gene-expression prediction.
  • Clinical decision support: AI-based facial phenotyping system for rare genetic disorders, including prototype development, validation, and collaboration with clinical experts.
  • Industrial Computer Vision: Pedestrian detection models for vehicle safety in collaboration with Robert Bosch GmbH.
  • AI governance and high-risk AI documentation: Risk classification, documentation, governance, and validation concepts under EU AI Act, ISO/IEC 42001, and GDPR.

Einsatzorte

Einsatzorte

Heidelberg (+75km) Munich (+75km)
Deutschland, Schweiz, Österreich
möglich

Projekte

Projekte

2 years
2024-04 - 2026-03

Developed reproducible Python pipelines

Research Associate, Medical Computer Vision
Research Associate, Medical Computer Vision
  • Developed reproducible Python pipelines for high-throughput Computational Pathology and spatial transcriptomics (Visium HD), including cell segmentation, cell-type annotation, and gene-expression prediction in containerized workflows.
  • Built aRAG/LLMpipelinetostandardizeapproximately16,000free-text surgical descriptions to SNOMED CT; designed the data integration layer and evaluated outputs for correctness and practical usability.
  • Developed and evaluated foundation models for surgical video understanding using Vision Transformers, self-supervised learning, and Vision-Language approaches.
  • Workedinaninterdisciplinary medical AI environment with requirements from research, clinical workflows, data integration, validation, documentation, and reproducibility.
German Cancer Research Center (DKFZ), Heidelberg
2 years 7 months
2021-09 - 2024-03

AI project on facial phenotyping

Postdoctoral Researcher, Human-Centered Artificial Intelligence
Postdoctoral Researcher, Human-Centered Artificial Intelligence
  • Led an AI project on facial phenotyping for rare genetic disorders; improved clinical experts? diagnostic accuracy by 20%, built a clinical decision-support prototype, and coordinated validation with the National Institutes of Health (NIH, USA).
  • Planned research milestones, coordinated interdisciplinary collaboration, and translated AI research results into a clinically relevant prototype.
  • Conducted additional research on 3D face shape modeling for emotion and action-unit recognition.
University of Augsburg, Augsburg
3 years 8 months
2017-08 - 2021-03

Developed AI models

Doctoral Researcher, Learning and Behavior Analysis
Doctoral Researcher, Learning and Behavior Analysis
  • Developed AI models for automated student engagement estimation using multimodal real-world classroom data; published and presented results at international venues.
  • Designed and built a multi-camera IP network for real-time audiovisual data acquisition; led data collection at partner schools, drafted the ethics application, and implemented data-protection requirements.
  • Gained practical experience in multimodal sensing, video analysis, experimental design, data protection, and real-world AI validation.
University of Tübingen, Tübingen
6 months
2019-01 - 2019-06

Conducted research on deep graph neural networks and equivariance properties

Visiting Researcher
Visiting Researcher
Brown University / ICERM, Providence, Rhode Island, USA
2 years 7 months
2015-01 - 2017-07

Developed self-supervised learning methods

Research Associate, Deep Learning
Research Associate, Deep Learning
  • Developed self-supervised learning methods for human pose estimation and video retrieval.
  • Built pedestrian detection models for vehicle safety in collaboration with Robert Bosch GmbH, connecting deep learning research with industrial computer vision use cases.
Heidelberg University, Heidelberg
3 years 4 months
2010-09 - 2013-12

Operated and maintained electronic command

Technical Officer, Electronics
Technical Officer, Electronics
  • Operated and maintained electronic command, control, communication, and sensor systems on a frigate and a fast patrol boat.
  • Served as watch officer with responsibility for technical readiness, reliability, and operational discipline in mission-critical environments.
Turkish Naval Forces, Turkey

Aus- und Weiterbildung

Aus- und Weiterbildung

3 years 7 months
2017-08 - 2021-02

Ph.D. in Computer Science (magna cum laude)

University of Tübingen, Tübingen
University of Tübingen, Tübingen
2 years 2 months
2012-08 - 2014-09

M.Sc. Electronics Engineering

Istanbul Technical University, Istanbul, Turkey
Istanbul Technical University, Istanbul, Turkey
4 years 2 months
2006-07 - 2010-08

B.Sc. Electronics Engineering (Control Systems)

Turkish Naval Academy? National Defense University, Istanbul, Turkey
Turkish Naval Academy? National Defense University, Istanbul, Turkey

Kompetenzen

Kompetenzen

Top-Skills

Künstliche Intelligenz Machine Learning Bildverarbeitung Deep Learning Deep Neural Network Python PyTorch Large Language Models RAG Multi-Agent Systems Natural Language Processing Computer Vision Medical Image Processing Vision Transformers AI Governance ISO/IEC 42001 LangChain MLOps Docker Object Detection Image Segmentation AI Management System

Produkte / Standards / Erfahrungen / Methoden

Profile

  • Senior AI/ML Engineer and Applied Scientist with a Ph.D. in Computer Science, an engineering background in electronics, and 10+ years of hands-on experience in applied machine learning, deep learning, Computer Vision, medical AI, LLM/RAG systems, and data-intensive AI pipelines.
  • Experienced in delivering AI solutions from requirements analysis, feasibility assessment, data and model design, implementation, training, evaluation, and validation to documentation, reproducible workflows, and deployment-oriented handover.
  • Strong practical background in Python, PyTorch, deeplearning, VisionTransformers, self-supervised learning, Lang Chain/LangGraph, LlamaIndex, Docker, MLflow, cloud-based ML workflows, and interdisciplinary AI projects with clinical, scientific, and industrial partners.
  • TÜV-certified AI Officer (KI-Beauftragter) with knowledge of EUAI Act, ISO/IEC42001, GDPR, risk classification, audit trails, AI documentation, and governance requirements for trustworthy and regulated AI systems.


Freelance Project Focus

  • Preferred roles: AI/ML Engineer, Machine Learning Engineer, Computer Vision Engineer, LLM/RAG Engineer, MLOps Engineer, Data Scientist, Applied AI Consultant, AI Governance Consultant, KI-Beauftragter.
  • Project types: AI proof-of-concepts, production-oriented ML prototypes, LLM/RAG systems, computer vision pipelines, medical and scientific AI applications, model evaluation, MLOps workflows, AI governance documentation, and EU AI Act readiness.
  • Availability: immediately; remote, hybrid, or on-site in Germany by agreement


Technical Skills

  • AI/ML & Computer Vision: PyTorch, deep learning, Computer Vision, Vision Transformers, selfsupervised learning, vision-language models, medical image analysis, surgical video analysis, computational pathology, human pose estimation, pedestrian detection.
  • LLM, NLP &Agentic Systems: Large Language Models, Retrieval-Augmented Generation (RAG), LangChain, LangGraph, LlamaIndex, OpenAI and Anthropic APIs, Codex and Claude Code, SNOMEDCT standardization, multi-agent systems.
  • Programming & Data Engineering: Python (OOP, Clean Code), C/C++, CUDA, Bash, Git, CI/CD, testing, REST APIs, HDF5, PostgreSQL, MongoDB, data-intensive pipeline development.
  • MLOps, Cloud & Reproducibility: Docker, MLflow, Weights&Biases, AWS, AzureML, multi-node training, containerized workflows, experiment tracking, reproducible AI pipelines.
  • AI Governance & Compliance: EUAIAct, ISO/IEC42001, GDPR, riskclassification, AI documentation, validation, audit trails, governance processes for high-risk and regulated AI systems.


Selected Project Themes

  • LLM/RAG for medical text standardization: RAG pipeline for mapping approximately 16,000 free text surgical descriptions to SNOMED CT, including data integration and output evaluation.
  • Medical Computer Vision and Computational Pathology: AI pipelines for spatial transcriptomics, cell segmentation, cell-type annotation, and gene-expression prediction.
  • Clinical decision support: AI-based facial phenotyping system for rare genetic disorders, including prototype development, validation, and collaboration with clinical experts.
  • Industrial Computer Vision: Pedestrian detection models for vehicle safety in collaboration with Robert Bosch GmbH.
  • AI governance and high-risk AI documentation: Risk classification, documentation, governance, and validation concepts under EU AI Act, ISO/IEC 42001, and GDPR.

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