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PingWind

AI DevOps Engineer

Rockville, MDfull-timemidAdded today

About this role

PingWind seeks an AI DevOps Engineer to support AHRQ's AI/ML strategy in Rockville, MD. You'll build CI/CD pipelines, manage cloud infrastructure, and develop automation solutions while collaborating across teams to deliver reliable AI-powered applications.

What you'll do

  • Design and maintain continuous integration and deployment pipelines
  • Manage server, network, and cloud infrastructure configuration, patching, and upgrades
  • Develop reusable automation code and infrastructure-as-code solutions
  • Monitor and optimize application and infrastructure performance
  • Collaborate with developers, DBAs, and stakeholders on AI/ML solution deployment
  • Participate in Agile ceremonies and provide mentorship on DevOps best practices

What they're looking for

  • AWS (EC2, S3, CloudFormation, RDS, IAM, VPC, CloudWatch)
  • CI/CD pipeline design and implementation
  • Linux/Unix and Windows Server administration
  • Scripting languages (Python, Bash, PowerShell, Ruby, Perl)
  • Version control (Git, TFS, SVN)
  • AI/ML systems architecture and integration
  • Web servers (Apache HTTPD, NGINX, Java containers)
  • Database management and optimization

Benefits

  • Eleven federal holidays
  • Paid time off accrued per pay period
  • Parental leave
  • Three medical plan options with employer contribution
  • Dental and vision insurance
  • Short-term and long-term disability coverage
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PingWind

PingWind builds SaaS/PaaS solutions and business intelligence dashboard systems, with work supporting government agencies including the Veterans Benefits Administration. The company is hiring quality assurance engineers, front-end developers, DevSecOps engineers, and business intelligence software engineers for roles emphasizing secure infrastructure, user experience, and data visibility.

View all jobs at PingWind

Likely interview questions

  • Describe your experience designing CI/CD pipelines for AI/ML applications and the tools you've used.
  • How have you approached integrating and deploying machine learning models in cloud environments like AWS?
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