ML DevOps Test Software Data
# Summary
I’m a full-stack engineer and AI infrastructure specialist with experience in building scalable systems, automating workflows, and creating data-driven solutions. My work includes developing self-service platforms, AI agents, and optimizing processes in both software and hardware. I focus on practical, efficient solutions and adapt quickly to new technologies.
# Skills
| Category | Skills |
|---|---|
| Programming | Python, C/C++, MATLAB, TypeScript, JavaScript, R, Bash/Shell/CShell Scripting |
| Frameworks & Libraries | Pandas, NumPy, Matplotlib, Seaborn, Django, React JS, Next JS, Svelte, Flask, Regex, SQLite, FastMCP |
| Databases | PostgreSQL, MySQL, Firebase, MongoDB, SQLite |
| Machine Learning | TensorFlow, PyTorch, Scikit-learn, Keras, Fundamental and abstract understanding of CNNs, RNNs, hyperparameter tuning, regularization, optimization techniques, and best practices for structuring and scaling machine learning projects. |
| DevOps & Tools | Git, Docker, CI/CD Pipelines, Linux Server Management, Gunicorn, Nginx, Apache, WebSockets, Agile, Jira, Confluence, LabVIEW, ROS, Modbus, Altair Monitor |
| AI Infrastructure | RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), API Security, Prompt Engineering |
| Web Development | Full-Stack, RESTful APIs, Authentication Systems (Okta, LDAP, OAuth), Caching, Optimization, Software Architecture |
| EDA License Management | License Server Administration, Usage Auditing, Automated Alerting, AI-Driven Support Systems, FlexLM |
| Certifications & Courses | Neural Networks & Deep Learning, Statistical Consulting, Deep Learning Specialization (Stanford Online) |
# Experience
Download Resume# Projects
Neural Network Visualizer
- Developed a live neural network visualization tool using TypeScript, allowing real-time interaction with neuron activations through mouse movement and drawing.
- Implemented forward propagation from scratch, demonstrating a strong understanding of neural network mechanics.
- Integrated weights trained in Google Colab into a custom backend function, enabling dynamic network building and visualization in the frontend.
- Enhanced user experience by providing intuitive visual feedback of neuron activations and network behavior.
- 2D graph label predictor and hand-written digit recognizer.
- Technologies: Typescript, NextJS, Python, Numpy, Tensorflow, Pandas, Matplotlib
3D Portfolio
Simple Todo List
- Simple yet powerful todolist that utilizes the browser's local database to store tasks (Indexed DB).
- One input can control due date, priority, cycle, and label of tasks.
- Tasks that are finished are automatically archived, unless it repeats.
Portfolio (this)
- This portfolio site is running on a raspberry pi.
- Technologies: Python, Nginx, Gunicorn, Flask, and Postgres.