[Remote] Lead, Machine Learning Ops Engineer

Note: The job is a remote job and is open to candidates in USA. The Planet Group is seeking a Lead Machine Learning Ops Engineer to drive their enterprise AI/ML platform strategy. This hands-on leadership role involves guiding a team and ensuring AI systems are built and governed with reliability and efficiency, while also engaging in advisory and planning activities.

Responsibilities

• Drive the enterprise AI/ML platform strategy as a player/coach
• Lead—guiding a team of Machine Learning Operations Engineers while shaping end-to-end execution
• Spend 70% advisory/planning and 30% keyboard and special initiative project work
• Ensure AI systems are built, operated, and governed with reliability, scalability, security, compliance, and cost efficiency—all aligned to business goals

Skills

• Lead experience delivering MLOps platform strategy, execution, and roadmap guidance
• Advanced proficiency in Python
• Object-oriented architectural mastery across dynamically typed languages
• Experience integrating and governing multi-language systems, including Python and JavaScript/TypeScript (enterprise platforms such as .NET experience helpful)
• Leadership-level expertise in AI/ML platform engineering, including MLOps, LLMOps, and AIOps
• Ability to define and enforce enterprise standards for AI model lifecycle management, including: monitoring and reliability, governance, cost control
• Deep understanding of AI system observability, including: drift detection, evaluation frameworks, incident response
• Strong experience with cloud architecture, security, compliance, and enterprise-scale deployments
• Proven ability to guide technical decision-making and platform strategy
• 6+ years of relevant experience
• Experience in MLOps, DevOps, or related fields with a focus on enterprise-level solutions
• Supervisory experience Highly preferred
• Experience specifically strengthening enterprise practices across generative AI and LLM-based systems
• Demonstrated ability to translate observability and operational learnings into continuous platform improvements

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