PyTorch Experts in Nuremberg

David O.

AI Engineer

Last position:

Machine Learning Researcher at Pattern Recognition Lab, FAU

Fine-tuned and evaluated alternative backbone architectures for the AFFGAN handwriting style encoder to better capture writer-specific features, benchmarking against the original baseline architecture and replacing the legacy VGG19 architecture with a custom Transformer-based encoder.
Extended the system beyond word-level synthesis to line-level handwriting generation, integrating and adapting an existing line-generation architecture to support full-sentence output.
Deployed the handwriting generation pipeline as a web application, enabling users to select a writing style, input custom text, and instantly generate a corresponding handwriting image.
Integrated TrOCR into a teacher–student distillation framework to evaluate and improve readability of generated handwriting, improving OCR accuracy by 20% over the baseline.
Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.

Tech: PyTorch, Python, GANs, CNN, ViT, TrOCR, Streamlit, CUDA.

Muntaha S.

AI Engineer (Freelance)

Last position:

AI Engineer (Freelance) at Upwork

Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.

Pawan S.

Academic Project

Nuremberg

Last position:

CAPTCHA Recognition using CRNN

Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib

Arun Sai T.

AI-Backend Developer Intern

Last position:

AI-Backend Developer Intern at Calvergy UA

Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.

Kashyap K.

Master’s Thesis - Synthetic Data Generation for Quality Inspection

Nürnberg

Last position:

Master’s Thesis - Synthetic Data Generation for Quality Inspection at Schaeffler Technologies AG

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.

Aqsa Y.

Client Services Manager

Last position:

Multilingual Translation Tool - NLP Project

Integrated MarianMT (Marian Machine Translation) models to ensure high-quality neural machine translation (NMT).
Managed model loading and tokenization via Hugging Face Transformers, optimizing for offline caching and reproducibility.
Planned extensions: language auto-detection, batch translations, and streamlined GPU inference with PyTorch.

Puranjan B.

Internship - Generative AI

Last position:

Internship - Generative AI at Continental

Gathered tire images and their feature descriptions.
Cleaned dataset of image metadata using pandas.
Stored image feature embeddings in Chroma vector db.
Used image augmentations to increase dataset size.
Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
Used PyTorch to train and test different neural networks.
Validated model using custom accuracy metric based on similarity search in ChromaDB.
Visualized accuracy predictions using matplotlib.
Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
Created custom Docker image in Amazon Elastic Container Registry for machine learning script.

Uddipan B.

Research Team Member

Last position:

Research Team Member at Munich Music Labs, TUM

Focused on exploring the intersection of Music and AI.

Musaib P.

Research Intern – Exploring Reasoning with Diffusion Models

Last position:

Research Intern – Exploring Reasoning with Diffusion Models at Machine Learning and Perception group, FAU Erlangen-Nürnberg

Investigating the equivalence between the Tiny Reasoning Model (TRM) and diffusion models for structured reasoning tasks such as Sudoku and maze solving.
Exploring the reasoning and generative capabilities of diffusion models in symbolic problem-solving environments.

Ekaansh K.

Master thesis - LLM powered RAG System