Python libraries (pandas, trimesh, plotly, meshlab, PyTorch Geometric) plus 3D and simulation workflows: mesh autoencoders, SDF, and FEM data.
About
I am an AI Solutions Engineer with a PhD in Artificial Intelligence, focused on building practical, reliable AI systems for real-world applications.
My work centers on industrial computer vision, data-centric AI, and end-to-end system design: from problem definition and dataset construction to model development, evaluation, integration, and deployment.
I specialize in improving model performance through data refinement and in designing hybrid systems that combine vision, geometry, and language models.
PhD in Artificial Intelligence
Technical University of Chemnitz
Applied AI Focus
Industrial computer vision, data-centric AI, and production-oriented workflows
Applied & Scientific Foundation
Practical AI for real-world systems, grounded in scientific machine learning and research methods
Core Expertise
Computer Vision
Object detection, instance segmentation, industrial image understanding, field-condition evaluation, and visual inspection workflows.
Data-Centric AI
Dataset design, annotation strategy, class balancing, failure-case analysis, and iterative model improvement.
AI Systems
End-to-end pipelines, workflow integration, production-oriented design, validation logic, and deployment planning.
Multimodal AI
Vision-language workflows: semantic retrieval, document/PDF understanding, and structured extraction from mixed media.
Deployment
CoreML, Swift/iOS integration, on-device inference, model export, and mobile visualization prototypes.
Scientific ML
FEM surrogate modeling, 3D deformation learning, mesh autoencoders, implicit representations, and RL for process modeling.
Skills
PyTorch, TensorFlow, and scikit-learn for model development, plus computer vision: detection, segmentation, industrial imagery, preprocessing, and evaluation.
Frameworks
Docker, conda, Linux, Git, Jenkins, and Jupyter workflows; multimodal AI with RAG, CLIP, document/PDF understanding, and local LLMs (Ollama, Unsloth).
ML Tools
OOP and languages including C#.NET, C++, MATLAB, and Python; deployment with CoreML, Swift/iOS, on-device inference, and model export.
Programming Skills
Research & Education
PhD in Artificial Intelligence
Technical University of Chemnitz, Germany | 2026
Thesis: Exploring Deep Learning Approaches for 3D Deformation: Toward Finite Element Method Distillation
M.Sc. Mechatronics Engineering
K.N. Toosi University of Technology, Iran | 2010
Thesis: Visual Servoing and Object Pose Estimation - 6 DOF Robot Manipulator
B.Sc. Computer Software Engineering
IAU Tehran Branch, Iran | 2006
Thesis: Online Persian Handwriting Recognition using Neural Networks
Professional Experience
AI Solutions Engineer - TKI mbH, Germany
2024 - Present- Develop AI solutions for industrial inspection, infrastructure analysis, document understanding, and semantic retrieval.
- Design computer vision systems for object detection, duct segmentation, color detection, and noisy real-world field imagery.
- Prepare deployment-oriented prototypes, including CoreML export and iOS visualization workflows.
- Improve model performance through data-centric AI: dataset refinement, annotation strategy, class balancing, and failure-case analysis.
Research Scientist - Technische Universitaet Chemnitz, Germany
2018 - 2024Research career focused on machine learning for engineering simulation, 3D deformation modeling, automotive body production, and environmental sensor analysis across BMBF-funded applied research projects.
ML@Karoprod: Machine Learning for Automotive Body Production
BMBF project with Fraunhofer IWU, SCALE GmbH, and TU Chemnitz.
- Created large-scale FEM training datasets for sheet metal forming where no suitable dataset existed.
- Used implicit neural representations, including signed distance functions, for dense deformable geometries.
- Developed surrogate models for geometric deviation, thickness distribution, and thinning on high-resolution shell meshes, including the ML-Karoprod MeshPredictor.
- Built reinforcement learning approaches for inverse process design and sequential deformation modeling.
Smart Airsense: AI-Based Health Assistant
BMBF project with Corant GmbH / air-Q and TU Chemnitz.
- Worked with air-quality sensor measurements logged over time by Corant / air-Q devices.
- Added machine learning methods for predicting environmental events from noisy real-world time-series data.
- Developed and validated predictive models in a human-in-the-loop machine learning setting.
Selected Applied Projects
Industrial Vision: Field Detection and Deployment Prototypes
Applied AI · 2024 - PresentBuilt field-ready computer vision workflows for construction-site and infrastructure imagery.
- Designed segmentation pipelines for trenches, ducts, fibers, and fittings in construction-site imagery.
- Handled lighting variation, occlusion, dirt, shadows, and ambiguous material boundaries during evaluation.
- Converted models to CoreML and built iPhone prototypes for on-device visualization and testing.
- Connected annotation strategy, model evaluation, failure analysis, and deployment preparation in one workflow.
Local LLM, Multimodal Document Understanding and Semantic Search
Applied AI · 2024 - 2026Developed privacy-conscious local AI workflows for documents, images, and knowledge extraction.
- Built local LLM-based workflows for document understanding, semantic search, and knowledge extraction without external cloud inference.
- Extracted structured information from complex technical documents, PDFs, and tables.
- Combined retrieval, vision-language models, and RAG-style approaches for automated document analysis.
- Built CLIP-based semantic image search using natural language queries, embeddings, and similarity-based clustering.