DevOps Engineer

Role Summary:

Products impacted by this role range from customer insights informing new services to the most advanced autonomous driving systems in the industry.
Responsibilities:
Lead the DevOps efforts for a petabyte-scale data collection and machine learning infrastructure project that will power future intelligent driving systems.
Update, deploy, and monitor systems developed by the team, proactively looking for opportunities to improve performance, reliability, and automation.
Help move implementation through automotive industry development phases, from research to deployment supporting millions of cars on the road.
Analyze and support DevOps requirements from infrastructure developers and machine learning specialists, focusing on the most important topics in the rapidly evolving automotive industry.
Oversee day-to-day usage and provisioning of cloud resources, both in terms of fixed resource needs, as well as helping build systems for dynamic scaling based on project needs.
 

Required Skills and Experience:

Experience with key DevOps technologies including Docker, as well as DevOps experience in AWS, including Cloud Formation.
Broad skills and experience in deploying, debugging, and managing systems in cloud environments such as AWS.
Ability to build and maintain continuous integration (CI) and continuous deployment (CD) systems for complex, distributed applications, using tools like Jenkins.
Experience diagnosing and debugging applications in complex, distributed, heterogeneous computing environments.
Mastery of key development tools such as GIT, and familiarity with collaboration tools such as Jira and Confluence or similar tools.
Overall, 4+ years of industry experience working with cloud technologies and handling DevOps functions for complex systems.
Bachelors degree or equivalent required.
Desired Skills and Experience:

Familiarity with AWS Code Pipeline, AWS Code Deploy, and AWS CodeCommit CI/CD services.
Hands-on development with relevant distributed computing languages, frameworks, and libraries.
Experience with highly-scalable and distributed databases and the key issues affecting their performance and reliability.
Experience or understanding of developing machine/deep learning systems in a distributed environment.
Conventional backend development experience with Java and common frameworks (Spring, Play)