Software Engineer Resume
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Taylor, MI
TECHNICAL SKILLS
- Jenkins
- OpenShift/Kubernetes
- Docker
- Concourse
- Agile
- Linux
- Android Studio
- Git
- Python
- Java
- Bash Shell Scripting
- Azure
PROFESSIONAL EXPERIENCE
Confidential, Taylor, MI
Software Engineer
Responsibilities:
- Developed and maintained pipelines for continuous integration/delivery of FordPass/LincolnWay applications
- Automated various processes that have led to a significant increase in productivity and efficiency for the DevOps Team, among them:
- Wrote a Python script that automated the entire compliance audit process for all microservice and mobile app teams
- Original manual process took upwards of 60 hours to complete, and needed to be performed several times per year and was error - prone and had many gaps
- New automated process runs as an intermediate job on the pipelines with each check taking seconds instead of hours
- Process is proactive instead of reactive leading to elimination of noncompliance within the organization
- Docker image update for all regions/applications
- Wrote a shell script that runs on a time resource-triggered pipeline that automatically updates our docker image with the latest dependencies being used in the codebase
- A four hour weekly manual task was optimized to become a one hour daily automated task, saving approximately 200 hours of manual effort per year and improving efficiency and efficacy of the existing process
- This eliminated timeout issues on our build pipelines that resulted from trying to download the dependencies at runtime
- Automated the entire release branch cut and pipeline deployment process
- Release branch cut, Slack announcements, deployment of release pipelines and incrementing of master branch version number all now run at the click of a single button, and can even be scheduled to run automatically by creating an event on the calendar
Confidential, Dearborn, MI
Android Developer
Responsibilities:
- Part of a two-person development team working on a project in conjunction with Ford
- Developed a multi-modal sensor array application
- Streams data in real time from four different sensors
- ECG, Smart Watch, Ambient Temperature, and Smart Thermostat
- Data is streamed to a server-based incremental machine learning model
- Model adapts and will adjust the user’s environment based on sensor input and user preferences to maximize comfort in real time
- Application was built from the ground up
- Involved the integration of 4 different sensors from 4 different companies
- Implementations included JSON, REST, multithreading, OAuth, Bluetooth