my-site/content/resume/_index.md

---
title: "Resume"
date: 2019-02-11T07:50:51-05:00
draft: false
---
{{% resume/section "Work Experience" %}}<!--- {{{ -->

{{% resume/work-experience name="Red Hat" 
title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2020 — Aug 2021" %}}

- **Reduced deployment time by 66%** by implementing a 
  [solution](https://github.com/apache/incubator-kie-kogito-operator/commit/175a6356c5474f2360ccb8ae835e0b9b2d653cf1) for deploying locally-compiled binaries onto 
  Kubernetes/OpenShift via command-line, **cutting average 
  deployment times from 45 minutes to 15 minutes**. 
  (**Kubernetes/GoLang** used for this and three below). 
- **Eliminated 80% of manual configuration errors** by enabling 
  the Kubernetes operator to automatically fetch data from 
  deployed services and update configurations, **deprecating 
  legacy startup scripts and reducing overall startup time 
  by 40%**.
- **Improved application stability** by introducing startup 
  probes for legacy applications with longer boot times, 
  **resulting in a 50% reduction in startup-related failures 
  and downtime during production launches**.
- **Enhanced system reliability** by refactoring probes to 
  [assign default values](https://github.com/apache/incubator-kie-kogito-operator/commit/af4977af228ec8648be28779259d4552246b656f) dynamically based on deployed YAML 
  files and fixing reconciliation issues, **increasing probe accuracy by 30%** and preventing misconfigurations.
- **Increased CI pipeline efficiency** by rewriting the 
  **Jenkins (Groovy)** [nightly pipeline](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88) to run in a GitHub PR 
  environment, allowing for automated testing of all 
  team-submitted PRs prior to merging, **reducing manual 
  intervention by 60%**.
- **Increased project reproducibility** by taking initiative to 
  write a [reusable GitHub parameters file](https://github.com/apache/incubator-kie-kogito-pipelines/commit/4c83f1aecdea2c1ba2796b79839a90d4083dce88#diff-7d2c018dafbccec859077d19bf1ade53ec9c7649f235528ce89f5632b109f7e6) for the pipeline, 
  **enabling 100% reusability** and ensuring consistent pipeline 
  setups across different environments.

{{% /resume/section %}}<!--- }}} -->

{{% resume/section projects %}}<!--- {{{ -->

<!--- Rarity Surf {{{ -->

{{% resume/project name="Rarity Surf" 
languages="Python, Django, JavaScript, React"  
date="Oct 2021" show="true" %}}

- **Developed a full-stack web application** to generate rarity 
  rankings for NFT's integrated with leading NFT 
  marketplace's (OpenSea) API, 
  enabling users to **quickly identify rare NFT's** and check 
  their listing status, **improving market research efficiency by 80%**.
- **Architected a robust Django (Python) [backend](https://github.com/Kevin-Mok/rarity-surf)** to fetch and process 
  NFT metadata from IPFS, store rarity rankings in 
  **PostgreSQL**, and serve the data via GraphQL API, **ensuring low-latency access and scaling to handle 2,000+ concurrent requests**.

{{% /resume/project %}}

<!--- Rarity Surf }}} -->

<!--- {{{ Gobcog -->

{{% resume/project name="Discord Adventure Game" 
url="https://github.com/Kevin-Mok/astronofty" languages="Python" date="Jan 2020" 
show="true" %}}

- [**Redesigned item generation system**](https://github.com/Kevin-Mok/gobcog/pull/5) for open source Discord game 
  built with **Python**, replacing 83k-line static JSON files with dynamic item generation, achieving a **99% reduction** in file size and reducing memory usage by **85%**.
- **Implemented modular item components** to enable over **152,000 unique combinations**, improving gameplay diversity and item quality.

{{% /resume/project %}}

<!--- }}} Gobcog -->

<!--- Spotify Visualized {{{ -->

{{% resume/project name="Spotify Visualized" 
url="https://github.com/Kevin-Mok/astronofty" languages="Python, Django" date="June 2019" 
show="true" %}}

- **Built a [high-performance backend](https://github.com/Kevin-Mok/spotify-lib-vis)** in Python with Django, 
  utilizing Django ORM to model and manage user data 
  efficiently, processing over **10,000 tracks per library** via 
  the Spotify API.
- **Engineered and optimized database models** achieving a **50% reduction in query latency** on PostgreSQL 
  for core workflows through effective schema normalization.

{{% /resume/project %}}

<!--- Astronofty }}} -->


{{% /resume/section %}}<!--- }}} -->

{{% resume/section skills %}}<!--- {{{ -->

**Python**, **Django**, **JavaScript**, **React**, Node.js, PostgreSQL, MongoDB, Bash, **Git**, **Linux**, **Command Line**, Go(Lang), AWS, Kubernetes, Terraform, Docker (Compose), Jenkins, Groovy, Solidity, C  

{{% /resume/section %}}<!--- }}} -->

{{% resume/section education %}}<!--- {{{ -->

{{% resume/education name="University of Toronto (St. George)"
title="Computer Science Specialist — 3.84 GPA (CS). Graduated with High Distinction." date="2019 — 2024" %}}

{{% /resume/section %}}<!--- }}} -->

{{% resume/section "References" %}}<!--- {{{ -->

{{% resume/references %}}

{{% /resume/section %}}<!--- }}} -->

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