Staff ML Operations Engineer, Raft

• 5+ years of relevant hands-on experience
• 5+ years' experience with Docker and Kubernetes, provisioning production clusters and maintaining their compliance
• Practical experience with Machine Learning on Kubernetes
• Experience managing clusters of local GPU machines
• Experience building and maintaining machine learning platforms and pipelines
• Practical programming and scripting skills (Python preferred)
• Fast learner, analytical thinker, creative, hands-on, strong communication skills
• Able to work both independently and as part of a team
• Excellent problem-solving skills and attention to detail
• Proven experience with modern software development and engineering practices including scrum/agile, Git, and DevOps
• Ability to obtain a Security+ certification within the first 90 days of employment with Raft
• Ability to obtain and maintain a Top Secret clearance

Desirable

• We’re looking for an experienced Staff MLOps Engineer to support our customers and join our passionate team of high-impact problem solvers
• Raft is building a real-time data platform for the Department of Defense (DoD), aimed at enhancing operators' awareness of critical events, including instances like the Chinese balloon and Cessna over the White House incidents. Central to this data platform is the aggregation of real-time data from over 750 sensors.
• This data is subsequently enriched, rendered queryable, and ultimately presented as a common operational picture to empower operators in making time critical and pertinent decisions. Our system efficiently handles the processing of over a billion events daily, all achieved with millisecond-level latency. Key technologies include Kafka, Kafka Streams, Pinot, Java, Scala, and Kubernetes.
• Your involvement in this role will encompass hands-on collaboration with a team of accomplished individuals, collectively striving towards excellence. Your primary responsibilities will be to deploy ML infrastructure, build MLOps pipelines, and contribute to the development of a full-lifecycle ML platform