Principal MLOps Engineer

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Requirements:

• 7+ years of relevant hands-on experience.
• 5+ years experience with Docker and Kubernetes.
• 5+ years experience supporting enterprise Cloud applications or infrastructure (AWS, Azure, etc.).
• Solid understanding of Helm Charts.
• Practical experience with Machine Learning on Kubernetes.
• Experience managing clusters with GPU machines.
• Experience building and maintaining machine learning platforms and pipelines.
• Practical programming and scripting skills (Python preferred).
• Strong communication skills, analytical thinking, and creative problem-solving.
• Familiarity with modern software development practices including scrum/agile, Git, and DevOps.
• Ability to obtain a Security+ certification within the first 90 days.

Responsibilities:

• Deploy ML infrastructure.
• Build MLOps pipelines.
• Contribute to the development of a full-lifecycle ML platform.
• Collaborate with a team to enhance operators' awareness of critical events.
• Process large volumes of real-time data efficiently.