Full-Stack AI & Systems Engineer
Taking intelligent systems from architecture to production. Delivering robust full-stack applications, enterprise integration pipelines, and applied AI.
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Python
TypeScript
Node.js
FastAPI
PostgreSQL
Docker
PyTorch
TensorFlow
LangGraph
LlamaIndex
vLLM
Next.js
I build software that puts AI to work in real-world applications, from connecting language models to databases to building the interfaces people actually use.
My main focus is taking newer AI tools and plugging them into existing systems so they actually run reliably. Instead of just experimenting with models in notebooks, I work on the full setup: writing clean APIs, handling data retrieval, setting up secure connections, and making sure services can run on modern servers without breaking.
I have built projects ranging from enterprise CRM pipelines to computer vision tools with clear explanations for their results. I try to keep things practical, using tools like Python, TypeScript, Node.js, and Docker to ship software that works smoothly under real-world conditions.
Download Resume5+
Projects
12+
Technologies
2 years
Experience
Always
Learning
Jan 2025 – Present
Partner directly with founders and teams to take software from design to working production systems.
I handle the full build cycle: setting up backend services, integrating modern AI capabilities into existing codebases, building clean user interfaces, and deploying containerized applications that run without constant hand-holding. When an engineering team needs a custom feature or automation shipped fast and wired into their live environment, I build and deliver the whole vertical slice.
Oct 2024 – Jan 2025
Completed an intensive 3-month practical program focused on the fundamentals of machine learning, neural networks, and computer vision.
Hands-on coursework covered model evaluation, training convolutional neural networks, data preprocessing pipelines, and deploying computer vision models with interactive user interfaces.
A Node.js and Express middleware bridge built to reliably pipe inbound web submissions directly into Microsoft Dynamics 365 CRM. It handles Azure Active Directory OAuth 2.0 token handshakes, maps and sanitizes external form payloads to internal CRM fields, and isolates error boundaries so incoming records sync smoothly without manual entry. Deployed live on Railway.
An applied computer vision pipeline for brain MRI diagnostic support. Built using an Xception convolutional neural network to classify scans across four distinct categories (Glioma, Meningioma, Pituitary, and No Tumor). Integrated Gemini 1.5 Flash to automatically generate plain-language explanations of the visual patterns and reasoning behind each prediction, served through an interactive Streamlit interface.
