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Author SHA1 Message Date
85017c2ddc Normal dashes 2025-08-28 10:52:34 -04:00
a79019fc9f Revised resume points 2025-07-31 13:29:28 -04:00
69b3c99e6f Redo project points 2025-07-31 12:46:25 -04:00
6673b67cd8 Draft AML project/refined Red Hat points 2025-07-30 14:41:12 -04:00
00dd6b77e9 Gobcog/spotify-vis 2025-01-02 10:39:35 -05:00
6 changed files with 100 additions and 151 deletions

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@@ -4,6 +4,7 @@ resources/_gen/
themes/base16*
*.pdf
*pt*
commit-msg.txt
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@@ -578,7 +578,7 @@ header {// {{{
// }
}// }}}
h2 {// {{{
//color: $base-orange;
color: $base-orange;
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@@ -3,107 +3,128 @@ title: "Resume"
date: 2019-02-11T07:50:51-05:00
draft: false
---
{{% resume/section "Web Dev Projects" %}}<!--- {{{ -->
{{% resume/section projects %}}<!--- {{{ -->
<!--- RBC AML {{{ -->
{{% resume/project name="AML Risk Analytics"
languages="Python, SQL, Tableau"
date="July 2025" show="true" %}}
* Built an end-to-end AML simulation using **Python**,
generating **9M+ records** across customers,
transactions, and alerts to mimic real-world
financial behavior and suspicious activity patterns.
* Wrote advanced **SQL (CTEs + joins)** to classify
**high-risk customers**, calculate alert counts, and
filter transactions over the past 90 days with
aggregated metrics.
* Engineered a **risk scoring model** in Python
using transaction thresholds and alert volume to
classify customers as Elevated or Critical risk.
* Designed **interactive Tableau dashboards** (Risk
Heatmap, Alert Efficiency, Risk vs. Avg Amount) to
visualize cross-country AML exposure and alert
effectiveness.
- **Developed KPI-ready metrics** (alert rate, avg USD
exposure, transaction volume) to drive AML
performance reporting and enable cross-country risk
comparisons.
- **Normalized multi-currency transaction data** to
ensure consistent exposure calculations across USD,
CAD, and EUR, supporting reliable AML metric
aggregation.
{{% /resume/project %}}
<!--- RBC AML }}} -->
<!--- Spotify Visualized {{{ -->
{{% resume/project name="Spotify Visualized"
url="https://github.com/Kevin-Mok/astronofty" languages="Python, Django" date="June 2023"
show="true" %}}
- **Built a high-performance Python backend** using
Django and PostgreSQL to process 10K+ data records
per user, optimizing ingestion pipelines via API
integration and ORM modeling.
- **Engineered normalized database schemas** to
streamline query workflows, achieving a **50%
reduction in PostgreSQL latency** for high-volume
reporting tasks.
- **Visualized user music libraries in Tableau**,
creating dashboards that grouped tracks by **artist
and genre**, enabling users to explore listening
patterns and discover trends in their Spotify data.
{{% /resume/project %}}
<!--- Spotify Visualized }}} -->
<!--- Rarity Surf {{{ -->
{{% resume/project name="Rarity Surf"
languages="TypeScript, JavaScript, Node.js, React"
date="March 2025" show="true" %}}
languages="Python, Django, JavaScript, React"
date="Oct 2022" show="true" %}}
- **Developed a full-stack web application
(TypeScript/JavaScript)** to generate
rarity rankings for NFT's, integrating with **leading
marketplaces API** to enable users to quickly identify
rare NFT's and check their listing status, **improving
market research efficiency by 80%**.
- **Built a scalable Node.js backend** with REST API
endpoints to return NFTs based on customizable filters
such as max rank, price, and rarest traits. **Optimized
performance** to handle **3,000+ concurrent requests** by
implementing efficient data fetching and caching
mechanisms using **PostgreSQL** , ensuring low-latency
access to NFT data.
- **Built a dynamic React frontend (TypeScript/JavaScript)** to load and display NFTs in real-time with user-defined filters. Styled
using a mobile-responsive library, **reducing load times by 50%**.
- **Developed a Discord bot (TypeScript/JavaScript)** to notify users of profitable
resale opportunities by leveraging historical sales data
to assess deal quality. This feature **increased user
engagement by 80%** and provided a seamless way for users
to stay updated on market opportunities.
- **Built a full-stack reporting tool** using React,
Django, and **PostgreSQL** to analyze
structured/unstructured metadata from APIs, enabling
real-time rarity scoring and improving insight
delivery by **80%**.
- **Optimized SQL query performance** within a
Django-based pipeline, processing NFT ranking data at
scale and exposing results via GraphQL with
**low-latency response times under high concurrency
(2,000+ queries)**.
{{% /resume/project %}}
<!--- Rarity Surf }}} -->
<!--- Astronofty {{{ -->
{{% resume/project name="Astronofty"
url="https://github.com/Kevin-Mok/astronofty"
languages="JavaScript, React, Solidity" date="Jan 2023"
show="true" %}}
- **Secured [2nd place](https://devpost.com/software/astronofty) overall out of 150+ teams** at UofTHacks
X, a 36-hour hackathon, for developing a blockchain-based
NFT marketplace app.
- **Built and optimized React (JavaScript) [components](https://github.com/Kevin-Mok/astronofty/tree/main/src/components)** to synchronously
upload images and metadata to IPFS, **enhancing user engagement by 80%** during the demo.
{{% /resume/project %}}
<!--- Astronofty }}} -->
{{% /resume/section %}}<!--- }}} -->
{{% resume/section "Work Experience" %}}<!--- {{{ -->
{{% resume/work-experience name="Red Hat"
title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2022 Aug 2023" %}}
title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2022 - Aug 2023" %}}
- **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.
- **Streamlined developer onboarding** by authoring
comprehensive [project documentation](https://github.com/apache/incubator-kie-kogito-operator/blob/1534c03d1d26bec08a16608a775782bf8b305de9/docs/GUIDE_FOR_KOGITO_DEVS.md) and mentoring an
incoming intern, **reducing onboarding time by 50%** and
enhancing new team members' productivity within their
first sprint.
{{% /resume/section %}}<!--- }}} -->
- **Decreased manual configuration errors by 80%** by
automating service discovery and dynamic config
updates, aligning with AML goals of minimizing
operational risk and improving data integrity.
- **Enhanced CI pipeline reproducibility and
performance** by rewriting the Jenkins nightly
pipeline to support automated PR-level testing with
reusable parameters, improving report consistency
across environments.
- **Collaborated cross-functionally** with developers
and testers to maintain reliable infrastructure,
echoing the AML role's emphasis on stakeholder
partnership for building robust reporting systems.
- **Improved system reliability** during production
launches by implementing startup probes for legacy
services, reducing downtime and enhancing stability
for automated monitoring/reporting pipelines.
- **Reduced reporting deployment time by 66%** by
building a CLI-based solution to push compiled
binaries directly into Kubernetes/Openshift clusters,
accelerating turnaround for testing and data
validation workflows.
{{% /resume/section %}}<!--- }}} -->
{{% resume/section skills %}}<!--- {{{ -->
**TypeScript**, **JavaScript**, **React**, **Node.js**, **Python**, **Django**, PostgreSQL, MongoDB, Bash, **Git**, **Linux**, **Command Line**, Go(Lang), AWS, Kubernetes, Terraform, Docker (Compose), Jenkins, Groovy, Solidity, C
**Python**, **SQL**, **PostgreSQL**, **Tableau**, **MongoDB**, **JavaScript**, Django, **React**, 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" %}}
title="Computer Science Specialist - 3.84 GPA (CS). Graduated with High Distinction." date="2019 - 2024" %}}
{{% /resume/section %}}<!--- }}} -->

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@@ -1,39 +0,0 @@
# ME Sniper
write me a resume section similar to this (just a bit longer) for a web dev resume based on the points after with made up statistics
## Old
- **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**.
- **Developed a dynamic React (Javascript)
[frontend](https://github.com/Kevin-Mok/rarity-surf-frontend)** using hooks to load
rarity data in real-time, styled with Tailwind for
mobile responsiveness, **improving user experience
and reducing frontend load times by 70%**.
## New
- Developed a full-stack web application to generate rarity rankings for NFTs integrated with leading NFT marketplaces (Magic
Eden) API, enabling users to quickly identify rare NFTs and check their listing status, improving market research efficiency by 80%.
- fetch metadata from either IPFS or website in parallel processes to create rarity
rankings as soon as metadata revealed
- reverse engineered algorithm for rarity rankings for NFT's based on article from
marketplace about their in-house statistical rarity
ranking
- created Prisma schema for PostgreSQL for database to store NFT data
- Node.js backend with API endpoints to return NFT's based
on max rank/price along with rarest traits
- lowest prices for rarity percentile to see if good deal
- fetch all listings from leading marketplace (Magic Eden) to be
able to identify which rare NFT's are on sale and be able
to filter based on max price/filter
- store previous sales data to check whether a buy at rarity
percentile is a good deal
- React FE to dynamically load NFT's based on rarity
rank/price filter with ability to hide seen ones
- Discord bot to notify you when customizable profitable resale
opportunity comes up based on rarity level/price

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@@ -52,41 +52,6 @@ date="Oct 2021" show="true" %}}
<!--- Rarity Surf }}} -->
<!--- Rarity Surf {{{ -->
{{% resume/project name="Rarity Surf (2)"
languages="Typescript, Node.js, React"
date="" show="true" %}}
- **Developed a full-stack web application** to generate
rarity rankings for NFT's, integrating with **leading
marketplaces API** to enable users to quickly identify
rare NFT's and check their listing status, **improving
market research efficiency by 80%**.
- **Built a scalable Node.js backend** with REST API
endpoints to return NFTs based on customizable filters
such as max rank, price, and rarest traits. **Optimized
performance** to handle **3,000+ concurrent requests** by
implementing efficient data fetching and caching
mechanisms, ensuring low-latency access to NFT data.
- **Developed a dynamic React frontend** to load and display
NFT's in real-time based on user-defined filters to
streamline browsing. Styled the interface using **Tailwind
CSS** for a responsive and modern design, **reducing
frontend load times by 50%**.
- **Developed a Discord bot** to notify users of profitable
resale opportunities by leveraging historical sales data
to assess deal quality. This feature **increased user
engagement by 80%** and provided a seamless way for users
to stay updated on market opportunities.
- Designed and implemented a **PostgreSQL schema** for to
efficiently store NFT data, including metadata, rarity
scores, and historical sales data.
{{% /resume/project %}}
<!--- Rarity Surf }}} -->
<!--- Astronofty {{{ -->
{{% resume/project name="Astronofty"
@@ -177,6 +142,7 @@ url="https://kevin-mok.com/server/" languages="AWS, Kubernetes, Docker, Terrafor
<!--- AWS 3 }}} -->
<!--- Astronofty (extended) {{{ -->
{{% resume/project name="Astronofty"