David O.-AI Engineer

Check rate
Experience
Machine Learning Researcher
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.
Computer Vision Researcher
Institute for Factory Automation and Production Systems (FAPS)
- Collaborated within a research team to execute systematic data cleaning on thousands of industrial coil-winding images — manually correcting label noise on a representative subset and leveraging Cleanlab Studio's autofix to propagate corrections across the remaining dataset, establishing a high-fidelity baseline for downstream model training.
- Trained and evaluated fully-supervised baseline classifiers (DINOv2-L and EfficientNetV2s) to establish reference macro-F1 performance, benchmarking against SSL (FixMatch/MixMatch) gains across four label-fraction splits (10%, 25%, 50%, 100%).
- Ran systematic hyperparameter optimization with Optuna across backbone/algorithm combinations, tuning learning rate, batch size, and augmentation parameters, and tracked all trials via a persistent SQLite results database for reproducibility.
- Designed and implemented a config-driven multi-label SSL training pipeline supporting both FixMatch and MixMatch algorithms, achieving a 90% macro-F1 average score with FixMatch.
Tech: PyTorch, DINOv2-L, EfficientNetV2-L, FixMatch, Optuna, WandB, Docker.
IT HiWi
FAU - Department of Chemistry and Pharmacy
- Secured and streamlined network infrastructure (LAN, WAN, VPN, firewalls, routers, switches), improving connectivity, reducing latency by 30%, and enhancing business continuity.
- Administered Active Directory and Group Policy Objects (GPO), managing user accounts, enforcing role-based access, and improving compliance with internal security policies.
- Provided Tier 1–3 technical support to end-users, resolving hardware, software, and network issues within SLA timelines, achieving a 95% user satisfaction rating.
- Conducted IT audits, compliance checks, and vulnerability assessments, ensuring 100% adherence to internal policies and regulatory requirements.
- Managed IT procurement, asset tracking, and lifecycle management.
- Coordinated with third-party vendors and service providers for escalated technical issues, maintaining 99% SLA compliance and ensuring uninterrupted business operations.
- Implemented network segmentation, VPN access controls, and MFA authentication, significantly improving security posture for remote and hybrid work environments.
Industry experience
See where this freelancer has spent most of their professional time.
Experienced in Education, Manufacturing, and Information Technology.
Business area experience
See which departments and functions this freelancer has contributed to most.
Experienced in Information Technology, Procurement, Research and Development, and Product Development.
Summary
AI engineer with an M.Sc. in Artificial Intelligence from FAU Erlangen-Nürnberg and 2+ years of PyTorch experience from research at the Pattern Recognition Lab and FAPS. I build and evaluate LLM systems: fine-tuned open-source models, RAG with hybrid retrieval and citations, and tool-using agents with guardrails and human approval. I benchmark models and measure quality, cost and latency rather than guess, build evaluation suites for retrieval and agent pipelines, and design human-in-the-loop workflows with deterministic guardrails and full tracing. I deliver clean, documented code. Based in Germany, fluent in English.
Skills
- Machine Learning & Deep Learning
- Cloud Deployment (Aws)
- Monitoring And Reproducibility Best Practices
- Computer Vision, Image Processing
- Llms & Prompt Engineering (Gpt, Llama, Claude, Gemini, Rag, Langchain, Langgraph, Llamaindex, Crewai, Mcp, Tool Calling, Structured Outputs With Pydantic)
- Data Engineering & Databases
- Generative Ai (Gans, Diffusion Models)
- Model Training, Evaluation & Performance Optimization
- Data Preprocessing, Augmentation, Feature Engineering
- Hyperparameter Tuning (Optuna), Model Validation
- Sql (Mysql, Postgresql), Mongodb, Prisma Orm
- Experiment Tracking & Versioning (Tensorboard, Wandb)
- Python, Node.Js, Ejs, Javascript, Typescript
- Html, Css, React, Next.Js, Tailwind Css
- Version Control (Git)
- Pytorch, Tensorflow, Scikit-Learn
- Opencv
- Hugging Face Transformers
- Vector Databases (Faiss, Qdrant, Pinecone, Chromadb)
- Numpy, Pandas, Matplotlib, Seaborn
- Fastapi For Model Serving
- Rest Apis
- Docker, Ci/Cd Pipelines, Github Actions
- Automation & Workflows (N8n, Make)
- Collaboration In Cross-Functional Teams
- Adaptability To New Tools & Emerging Ai Technologies
- Clear Communication Of Technical Concepts
- High Focus On Quality, Accuracy & Reproducibility
Languages
Education
Friedrich-Alexander-Universität Erlangen-Nürnberg
Master of Science · Artificial Intelligence · Erlangen, Germany
Federal University of Technology
Bachelor of Technology · Computer Science · Minna, Nigeria
Certifications & licenses
Building RAG Agents With LLMs
NVIDIA
GitLab CI/CD And DevOps For Beginners
Introduction To AWS
Statistics
Experience
Global experience
Expertise
Qualifications
Profile
Frequently asked questions
Have questions? Find more information here.
David is based in Erlangen, Germany and can operate in on-site, hybrid, and remote work models.
David speaks the following languages: English (Native), German (Advanced).
David has at least 3 years of experience. During this time, David has worked in at least 3 different roles and for 3 different companies. The average length of individual experience is 1 year and 10 months. Note that David may not have shared all experience and actually has more experience.
Based on recent experience, David would be well-suited for roles such as: Machine Learning Researcher, Computer Vision Researcher, IT HiWi.
David's most recent position is Machine Learning Researcher at Pattern Recognition Lab, FAU.
In recent years, David has worked for Pattern Recognition Lab, FAU, Institute for Factory Automation and Production Systems (FAPS), and FAU - Department of Chemistry and Pharmacy.
David is most experienced in industries like Education, Manufacturing, and Information Technology.
David is most experienced in business areas like Information Technology, Procurement, and Research and Development. David also has some experience in Product Development.
David holds a Master in Artificial Intelligence from Friedrich-Alexander-Universität Erlangen-Nürnberg and a Bachelor in Computer Science from Federal University of Technology.
David has 3 certificates. These include: Building RAG Agents With LLMs, GitLab CI/CD And DevOps For Beginners, and Introduction To AWS.
David is immediately available part-time for suitable projects.
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Calculated based on our freelancers’ daily rates as of 21 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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