NumPy 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

Amir A.

Master's Thesis

Nürnberg

Last position:

Master's Thesis at Friedrich-Alexander University

Analysis of the role of marriage as an informal insurance in Germany using econometric methods (supervisor: Prof. Dr. Harald Tauchmann).
Prepared and cleaned multiple panel datasets and extracted relevant variables to create a final panel dataset with over 36,000 observations (2006–2020, SOEP).
Designed an IV model in Stata to examine the causal link between couple separation and health shocks (mental/physical).
Conducted robustness checks to ensure stability and validity of the results.

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.

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.

Vasuraj B.

Cloud Data Analyst

Last position:

Cloud Data Analyst at Bhatia Reply

Analyzed 50K+ customer records using SQL and Python in a cloud services firm, identifying trends
Designed interactive Tableau dashboards for sales and marketing stakeholders, reducing report
Developed ARIMA and AutoARIMA time series models to forecast AWS resource utilization, cutting
Automated ETL pipelines with Python, improving workflow efficiency by 20% for scalable data
Collaborated with DevOps teams to deploy 3 machine learning models in production using Docker

Ekaansh K.

Master thesis - LLM powered RAG System

Last position:

Master thesis - LLM powered RAG System at Friedrich-Alexander-Universität Erlangen-Nürnberg

Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS

Anshul P.

BSc Artificial Intelligence