Work with 8 different internal customer teams to identify problems and opportunities. Coordinate with 3 internal partner teams to clarify R&R and ensure de-duplication of work as the AI Platform evolves.Define roadmap for department and OKRs for team with buy-in from internal customers, partners, and leadership. Define technical requirements, plan projects, and draw up wireframes for frontend, backend, and MLOps teams. Work closely with development team leads to identify gaps or issues in requirements and opportunities for feature and efficiency improvements. Communicate updates and issues to all stakeholders and solicit feedback.Work closely with technical leads to define system architecture for the annotation tools, data management tools, and data pipeline platform.

Communicate with stakeholders and collaborate with PMs to define technical requirements.Make work priority and manage customer expectations to balance delivery speed and team member workload.Mentor and manage team of 9 frontend, backend, and MLOps engineers.Support team with technical knowledge and hands-on work when necessary.Review resumes, conduct interviews, and review take-home projects to build a best-of-class team to support our machine learning platform.Run developer culture task force to increase software quality, increase speed of delivery, increase morale, improve recruiting, and reduce churn throughout the organization. (In progress, metrics pending)Share various best practices and tech skills with the team via presentations and code samples on topics like software testing, Git hooks, infrastructure as code, AWS, Kubernetes, Docker security, security around secrets, documentation tools, and anything else needing improvement. (More topics pending)

Expand and support backend of machine learning annotation service using FastAPI and MongoDB Atlas.Automate and manage infrastructure, CI, and CD of ML annotation service on Kubernetes on AWS EKS and on-premise using Terraform, Helm, Ansible, BuildKite, and Github Actions.Run developer culture task force to increase software quality, increase speed of delivery, increase morale, improve recruiting, and reduce churn throughout the organization. (In progress, metrics pending)Participate in AWS task force to reorganize and establish standards for AWS infrastructure throughout the organization.Share various best practices and tech skills with the team via presentations and code samples on topics like software testing, Git hooks, infrastructure as code, AWS, Kubernetes, Docker security, security around secrets, documentation tools, and anything else needing improvement. (More topics pending)Review resumes, conduct interviews, and review take-home projects to build a best-of-class backend team to support our machine learning platform.

I started CodeSeoul originally as a branch of LearnTeachCode LA with the goal of building an education-focused community of IT people in Seoul. Over time, we decided to separate, and we continue to serve the greater Seoul area. With CodeSeoul, I mostly run events and help build the community.

Design, build, and maintain API services and backing data storage on Google Kubernetes Engine and Google Cloud SQL, respectively, written in NodeJS Design, build, and maintain Elastic Stack living on Kubernetes (Google Kubernetes Engine) using Terraform and HelmDesign, build, and maintain API services and backing data storage living in AWS using Terraform written in Golang and NodeJSBuild and maintain daemons written in Python and Golang on Raspberry Pi and custom hardware to communicate with remote servicesAdvise on software architecture in custom hardware systemsDesign, build, and maintain DevOps pipeline using Docker, Gitlab, Gitlab CI, Terraform, and AWS ECR/ECSDesign, build, and maintain monitoring systems using custom-built APIs, Datadog, Linux tools, SMTP, and Slack

Assisted in development of microservices using Go and C#. Automated infrastructure using Terraform for numerous projects, from WordPress to machine learning systems. Setup data pipelines for public API service, a machine learning system, and general data analysis using AWS Lambda, SQS, S3, Redshift, and Kinesis. Helped deliver public API service written in Go, running on AWS ECS.

Develop and maintain a minute-resolution ETL system for consuming platform metrics and incident dataGenerate reports on platform performance and incidentsDevelop and maintain web apps for ad hoc data needsHave fun building awesome, high-value tools to keep wards on player experience

Develop server for turn-based mobile gameConsult on mechanics design and other aspects of designManage the backlog and keep tabs on the direction of development

Acted as internal product manager of Splunk.Became company's second SME in Nagios.Developed an automated CMDB and supporting components using MySQL, Perl, and Ruby on Rails

Built systems to measure KPIs for the ETL product and ETL Operations teamSupported and maintained the homebrewed ETL frameworkImplemented, managed, and drove monitoring platform with a single pane of glass for the NOCImplemented Splunk internally: trained users, drove expansion of the product, created dashboards for operational and business metricsUsing Splunk, reduced incident isolation time for two products by 80%Was company SME for CA Spectrum, CA eHealth, and Splunk