Jake Jazokas

Software developer and technology leader. I take products from concept to live deployment — leading teams, integrating critical systems, and shipping reliable solutions in fast-moving environments.

Education
B.C.S. Computer Science, Carleton University — High Distinction, 2022
Currently
Full stack developer at Maverick Games
Focus
Mobile apps, distributed microservices, real-time production systems

01 About

I'm a full stack software developer and technology leader who takes products from concept to live deployment — launching mobile apps, optimizing distributed microservices for real-time performance, and leading teams that turn ideas into production-ready solutions quickly and reliably, including my current role building full stack features at Maverick Games.

Day to day, I build with Go, TypeScript, JavaScript, Vue.js, and PostgreSQL — owning features end to end, from the database schema through the API to the interface on top. I've led development teams through launches and production incidents, keeping delivery moving when it matters.

Outside product work, I enjoy the deep technical end of the stack — my side projects include eBPF-based security policy generation and machine learning intrusion detection, both born from a habit of asking how systems work at the lowest level.

02 Projects

Sturents — blueprint illustration of the Android rental listing app and scraper pipeline

Sturents

AndroidNode.jsAWS

An Android application designed to scrape listings for student rentals from the internet and intuitively display them.

View on GitHub
BPFContain — policy pipeline from eBPF trace to kernel enforcement

Automatic Policy Generation for BPFContain

PythonLinuxeBPF

Automatically generate security policies for BPFContain based on the traced operations of any program, leveraging the capabilities of the enhanced Berkeley Packet Filter (eBPF).

View on GitHub
Human Counter — detected people with confidence boxes and a count of four

Human Counter

DjangoPythonOpenCV

A Django web application that can identify and count the number of people in uploaded photos and videos.

View on GitHub
ML intrusion detection — packet n-grams feeding a classifier verdict

Wi-Fi Intrusion Detection using Machine Learning

Pythonscikit-learnML

A machine learning implementation that classifies abnormal wireless network activity, using n-grams extracted from streams of packets.

View on GitHub

03 Skills

Languages
  • Go
  • Python
  • C
  • Java
  • JavaScript
  • TypeScript
  • Perl
  • Shell Scripting
  • Dart
  • Swift
Frameworks
  • Django
  • Node.js
  • Vue.js
  • TensorFlow
  • PyTorch
  • scikit-learn
  • Keras
  • OpenCV
  • Gin
  • Flask
  • Swing
  • RCP
  • Flutter
Cloud & DevOps
  • Kubernetes
  • Docker
  • GCP
  • AWS
  • Git
  • Drone
  • Jenkins
  • ArgoCD
Testing & Quality Engineering
  • Robot Framework
  • Cucumber
  • Selenium
  • Playwright
Leadership & Compliance
  • Agile
  • Jira
  • iGaming Compliance
  • GDPR Compliance
Databases
  • PostgreSQL
  • MongoDB
  • Firebase