Merge pull request #1 from morganwm/codex/update-resume-descriptions

Clean out deprecated resume files

Author: morganwm

.github/workflows/build-resume.yml

@@ -25,10 +25,10 @@ jobs:
           # Define variables for clarity
           DOWNLOAD_URL="https://www.gust.org.pl/projects/e-foundry/tex-gyre/heros/qhv2.004otf.zip"
           OUTPUT_FILE="tex-gyre-hero.zip"
-          EXTRACT_DIR="tex_gyre_hero_fonts"
+          EXTRACT_DIR="fonts"
 
           # Use curl to download the file to the specified output file
-          curl -sSL "$DOWNLOAD_URL" -o "$OUTPUT_FILE"
+          curl -L "$DOWNLOAD_URL" -o "$OUTPUT_FILE"
 
           # Create the extraction directory
           mkdir -p "$EXTRACT_DIR"
@@ -40,15 +40,15 @@ jobs:
           rm "$OUTPUT_FILE"
           
       - name: Verify extracted files
-        run: ls -R tex_gyre_hero_fonts
+        run: ls -R fonts
 
       - name: Install Typst
         uses: typst-community/setup-typst@v2
         with:
           version: latest
 
       - name: Compile resume
-        run: typst compile resume.typ ${{ env.FILE }} --font-path ./tex_gyre_hero_fonts
+        run: typst compile resume.typ ${{ env.FILE }} --font-path ./fonts
 
       - name: Upload PDF artifact
         uses: actions/upload-artifact@v4

resume.typ

@@ -43,8 +43,6 @@
   #education-entry(
     edu-data.degree,
     edu-data.institution,
-    edu-data.location,
     edu-data.date,
-    edu-data.at("details", default: none),
   )
 ]

src/content.typ

@@ -15,57 +15,46 @@
 )
 
 #let overview-description = [
-  I have a wide variety of experience with different technologies and aspects of the stack, from lower-level services in Go, API backends in Python, Node, and Java, to CI/CD with both binaries and Docker Images, Kubernetes management both directly and with Helm, AWS infrastructure and orchestration. I also enjoy working with customers and alongside partner teams to get a better understanding of a problem and hopefully come up with a solution that addresses what matters instead of a symptom of the root problem.
-
-  In my current role within Apple, I have worked on several different applications and services which all share the same goal, taking the enormous amount of information generated by our factories and allowing people to do something useful. This has taken many forms, a distributed real-time platform written in Go to help feed fraud detection ML models, backends for advanced analytical platforms in Python using Ray to help researchers draw business value, and most recently a new method for extracting, collecting, and organizing unstructured data to feed the growing wildfire that is AI/ML training.
+  Senior Software Engineer specializing in large-scale data platforms, distributed systems, and ML infrastructure. 10+ years building high-throughput ingestion pipelines, analytics platforms, and real-time inference systems in AWS and Kubernetes environments. Strong cross-functional partner experienced in translating ambiguous manufacturing and research requirements into resilient, production-grade systems.
 ]
 
 #let bear = (
-  title: "Design and Implementation of Large Scale Data Ingestion and Movement for Apple Manufacturing Data",
-  tools: "Golang, Linux tooling, Docker, AWS (S3, IAM), Python, Kafka, and exploration in Flink",
+  title: "Distributed Data Ingestion & Edge Systems",
   bullets: (
-    "Led Discovery and Requirements gathering from various hardware and testing customers",
-    "Led Research into existing network and compute infrastructure at manufacturing sites",
-    "Designed and Built a lightweight daemon in Go with tunable performance characteristics for use at manufacturing sites",
-    "Designed a fault tolerant system for coordinating the daemons running remotely on-sites",
-    "Set up various CI, Packaging, and CD pipelines to allow for deployment of services to very secure, isolated sites",
-    "Built an initial MVP which served to collect several terabytes of targeted data for compliance and research purposes",
-    "Worked with AWS Solutions Experts to design and implement 'IAM Credential Vending' to restrict access to S3 data dynamically",
+    "Designed and implemented lightweight Go daemon deployed to secure manufacturing sites for high-volume data extraction",
+    "Architected fault-tolerant coordination system for distributed edge nodes",
+    "Delivered MVP collecting multiple terabytes of compliance and research data",
+    "Designed IAM credential vending model with AWS to enforce dynamic S3 access controls",
+    "Built CI/CD and packaging pipelines for deployment to isolated environments",
   ),
 )
 
 #let ipa-ray = (
-  title: "Proof of Concept with AI/ML team for use of Ray with Data Analysis/Visualization Platform",
-  tools: "Python, Ray, Docker/Containers, S3, Parquet, Redis, CI/CD",
+  title: "ML Enablement & Ray Platform POC",
   bullets: (
-    "Built Python library to wrap/shim existing tools for use with Ray Model Endpoint as a Service",
-    "Developed sets of tools to connect Ray to our custom indexing and search for use with S3/Iceberg Data Lakehouse",
-    "Worked with existing customers to build out a suite of tools allowing them to migrate and take advantage of the additional features provided by ray.",
+    "Built Python abstractions enabling Ray-based distributed analysis",
+    "Integrated Ray with custom search/indexing over Iceberg/S3",
+    "Partnered with researchers to migrate workloads onto distributed compute",
   ),
 )
 
 #let insight-lab = (
-  title: "Implementation of Data Lakehouse Platform for extremely large-scale analytics on Apple Manufacturing Data",
-  tools: "Jupyter, Python, Spark, Kubernetes, Docker/Containers, Iceberg, Parquet, AWS (EKS, EC2, S3, IAM)",
+  title: "Large-Scale Analytics & Lakehouse Architecture",
   bullets: (
-    "Led coordination across teams to set up custom monitoring to ensure consistent performance.",
-    "Worked with AWS to optimize node packing to reduce idle compute.",
-    "Worked with users to determine requirements for a custom tooling to help them more easily use the platform.",
-    "Led experimentation of secondary index methods leveraging Rust based Polars for rapid lookups.",
+    "Co-led implementation of Iceberg-based data lakehouse for manufacturing analytics at scale",
+    "Optimized Kubernetes node packing with AWS to reduce idle compute costs",
+    "Developed custom indexing strategies using Rust/Polars for accelerated lookups",
+    "Coordinated cross-team monitoring and performance standards",
   ),
 )
 
 #let argus = (
-  title: "Highly Available/Scalable Realtime Inference Pipeline running on Apple Manufacturing Data",
-  tools: "Grafana, Prometheus, Splunk, Spark, Kubernetes, Helm, Golang, Docker/Containers, Linux/UNIX Tooling, AWS (S3)",
+  title: "Highly Available/Scalable Real-Time ML & Inference Infrastructure",
   bullets: (
-    "Worked with users and data scientists to determine appropriate scope and requirements",
-    "Set up pipeline to run arbitrary ML Docker Images on a combination of stored and incoming data in real-time (this was before it was cool and there were lots of tools to do it)",
-    "Set up ELT pipeline for extracting and aggregating values from manufacturing data",
-    "Worked with various operations teams to ensure best practices with CI/CD, HA, atomic/immutable deployments",
-    "Wrote Custom self-contained forwarder (library and image) to connect monitoring tools to Apple internal Alerting",
-    "Set up Monitoring/Logging Stack for distributed systems",
-    "Developed libraries for maintaining compatibility with S3 Crypto for internal Apple Object Store",
+    "Architected real-time ML pipeline capable of executing arbitrary containerized models",
+    "Built ELT pipelines for structured aggregation of manufacturing telemetry",
+    "Designed HA monitoring stack (Prometheus, Grafana, Splunk)",
+    "Developed internal S3 crypto compatibility libraries",
   ),
 )
 
@@ -74,6 +63,7 @@
   company: "Apple (Manufacturing Data Insight)",
   location: "Austin, Texas",
   date: "May 2019 - Present",
+  tools: "Go, Python, Kubernetes, Docker, AWS (EKS, EC2, S3, IAM), Linux Tooling, Kafka, Spark, Iceberg, Ray, Rust, Prometheus, Grafana",
   projects: (
     bear,
     ipa-ray,
@@ -82,25 +72,17 @@
   ),
 )
 
-#let social-solutions-sr = (
-  title: "Senior Software Engineer, Architecture",
+#let social-solutions = (
+  title: "Senior Software Engineer",
   company: "Social Solutions Global",
   location: "Austin, Texas",
-  date: "October 2018 - May 2019",
+  date: "May 2018 - May 2019",
+  tools: "Node, Docker, GraphQL, React, AWS (Cognito, Lambda, ECS, DynamoDB, CloudFormation, CloudWatch, CloudTrail)",
   bullets: (
     "Technical Lead for Ecommerce Team",
-    "Designed and Led Development on Identity Server with integrated MFA and email Domain verification [Cognito, Lambda]",
-  ),
-)
-
-#let social-solutions-dev = (
-  title: "Full-Stack Software Developer",
-  company: "Social Solutions Global",
-  location: "Austin, Texas",
-  date: "May 2018 - October 2018",
-  bullets: (
-    "Designed and developed an Enterprise grade (over $1 Million in pipeline per quarter) serverless Ecommerce Platform for SaaS hosted in AWS [CloudFormation, Lambda, DynamoDB]",
-    "Designed and developed an Account Management platform for Enterprise SaaS applications. [ECS, Docker, GraphQL]",
+    "Designed and Led Development on Identity Server with integrated MFA and email Domain verification",
+    "Designed and developed an Enterprise grade (over $1 Million in pipeline per quarter) serverless Ecommerce Platform for SaaS hosted in AWS",
+    "Designed and developed an Account Management platform for Enterprise SaaS applications.",
   ),
 )
 
@@ -109,9 +91,10 @@
   company: "Axial Commerce",
   location: "Austin, Texas",
   date: "October 2017 - May 2018",
+  tools: "C# Dotnet Core, React, Azure Pipelines",
   bullets: (
     "Developed and maintained an MVC structured web application with C# .NET Core backend and React.JS frontend, hosted in Azure",
-    "Automated CI/CD pipelines for the website/API and apps to the Google Play Store and Apple App Store [Azure Pipelines]",
+    "Automated CI/CD pipelines for the website/API and apps to the Google Play Store and Apple App Store",
   ),
 )
 
@@ -129,18 +112,15 @@
 
 #let work-experience = (
   apple,
-  social-solutions-sr,
-  social-solutions-dev,
+  social-solutions,
   axial,
   dow,
 )
 
 #let education = (
   (
-    degree: "Bachelors of Engineering: Mechanical Engineering",
-    institution: "The University of Texas",
-    location: "Austin, Texas",
-    date: "2011-2015",
-    details: (),
+    degree: "B.S. Mechanical Engineering",
+    institution: "The University of Texas as Austin",
+    date: "2015",
   ),
 )

src/templates.typ

@@ -47,10 +47,10 @@
 }
 
 #let job(title, company, location, date, tools, bullets) = {
-  block(breakable: true)[
+  block(breakable: false)[
     #grid(
       columns: (1fr, auto),
-      align(left)[#text(weight: "bold")[#title] -- #company], align(right)[#text(style: "italic")[#location -- #date]],
+      align(left)[#text(weight: "bold")[#title] -- #company], align(right)[#text(style: "italic")[#location | #date]],
     )
     #if tools != none [
       #v(-0.5em)
@@ -77,18 +77,10 @@
   v(0.5em)
 }
 
-#let education-entry(degree, institution, location, date, details) = {
+#let education-entry(degree, institution, date) = {
   block(breakable: false)[
-    #grid(
-      columns: (1fr, auto),
-      align(left)[#text(weight: "bold")[#degree]], align(right)[#text(style: "italic")[#location -- #date]],
-    )
-    #text(style: "italic")[#institution]
-    #if details != none [
-      #for detail in details [
-        - #detail
-      ]
-    ]
+    #text(weight: "bold")[#degree] \
+    #text(style: "italic")[#institution] | #date
   ]
   v(0.5em)
 }