JulianSchlickmann
Building end-to-end products — from APIs to polished interfaces — and exploring how AI reshapes what software can do. From computer vision to chat interfaces, I operate at the edge of intelligent software engineering.
01. about
Who I am & what I build
Software engineer with full-stack experience across React, Node.js, Python, and SQL. Currently building products at Zapier and studying Applied AI at UFPR. From YOLOv8 object detection to chat interfaces powered by LLMs, I'm obsessed with the intersection of clean engineering and intelligent systems.
Applied AI Focus
Postgraduate specialization at UFPR. From theory to production — PyTorch, computer vision, and LLM integrations.
Frontend Engineering
Building user-facing products at scale at Zapier. React, TypeScript, design systems, and performance.
Always Learning
Active researcher at the intersection of AI and web. Writing, experimenting, shipping — every week.
// Languages
// AI / ML
// Frontend
// Backend
// Tools
02. experience
Career & Education
Specialization in Applied Artificial Intelligence
CurrentPostgraduate specialization in Applied AI at one of Brazil's top federal universities. Coursework covers machine learning, computer vision, NLP, and applied deep learning. Bridging academic AI research with real-world software engineering.
Frontend Engineer
CurrentBuilding interfaces used by millions on one of the largest automation platforms. Planned and scoped the Reseller Portal, a self-serve tool for resellers to create and manage quotes on behalf of customers. Led enterprise initiatives including the Workspaces admin page and the Admin Center, giving enterprise accounts centralized control over workspace creation and admin functionalities — key drivers behind Zapier's enterprise plan growth.
Intermediate Frontend Engineer
Joined the defense team to triage and fix bugs across every product surface, cutting customer-reported issues and clearing the path for feature teams. Became the most senior frontend engineer on a new learning experience squad — mentoring developers, leading projects, and introducing a schema-first workflow that decoupled frontend from backend delivery. Championed accessibility across the organization, advocating for inclusive design in every product surface.
Node.js Development Program
Intensive development program focused on server-side JavaScript with Node.js. Covered REST API design, Express, databases, and modern JavaScript patterns — a bridge from full-stack work into dedicated frontend engineering.
Full-Stack Web Developer
Built a data-quality testing web application for one of British Columbia's health authorities, replacing manual test processes with automated validation and real-time feedback. Reduced human error in data verification and cut the time spent on test cycles across the Decision Support department.
Technologist in Systems Analysis and Development
Six-semester program with heavy emphasis on OOP, data structures, and database design, plus deep dives into computer architecture, operating systems, and networking. Graduated with a strong foundation in software engineering and systems analysis.
Software Developer
Spent 4.5 years developing and maintaining modules for Microsiga Protheus, an enterprise ERP platform used by factories and large corporations. Built features for both the desktop application (ADVPL) and web interface (JavaScript), and authored technical documentation for every release.
JavaScript Developer
Contributed to linkvan.ca, an open-source web app that connects Vancouver's vulnerable and homeless populations with shelters, free meals, showers, and other vital services. Built in partnership with the UBC Learning Exchange and the Downtown Eastside Literacy Roundtable.
03. projects
Things I've built
View all on GitHub04. blog
Writing & thinking
Is RAG Dead? Why Long Context Windows Haven't Killed Retrieval Augmented Generation
LLMs now support million-token context windows, so teams are questioning whether RAG is still worth the infrastructure complexity. Here's a breakdown of the real trade-offs.
Probability Distributions: The Math Behind AI Predictions
Binomial, Poisson, Normal, T-Student — the probability distributions that power statistical models and machine learning, with real examples.
Statistics for AI: The Fundamentals Every Developer Should Know
Populations, samples, variables, mean, variance — the statistical building blocks that power every AI model, explained for developers.
05. contact
Let's connect
Whether you have a project in mind, want to collaborate on something at the intersection of AI and web, or just want to say hi — I'd love to hear from you.