ML / DevOps Engineer Lead

Role Overview

The ML / DevOps Engineer Lead is responsible for operationalizing AI systems through robust MLOps practices, ensuring models move reliably from research into production. This role bridges data science, engineering, and infrastructure to enable scalable, secure, and high-performance AI deployments.

Key Responsibilities

• Responsible for building and managing CI/CD pipelines for ML model training, deployment, and monitoring.
• Responsible for designing scalable, reliable ML infrastructure and technical architecture.
• Responsible for overseeing model performance monitoring, drift detection, and continuous improvement.
• Responsible for implementing infrastructure-as-code, automated testing, and MLOps best practices.
• Responsible for leading and coordinating a technical sub-team delivering production-grade AI systems.

Required Experience & Skills

Required

• Strong experience in MLOps, DevOps, and cloud or hybrid infrastructure.
• Hands-on expertise deploying and maintaining ML systems in production.
• Proficiency with CI/CD pipelines, monitoring tools, and automation frameworks.

Desirable

• Experience supporting AI systems in regulated or mission-critical environments.
• Familiarity with data security and compliance considerations.

Who This Role Is For / Not For

This Role Is For...

• Engineers who excel at operationalizing AI systems at scale.
• Technical leaders experienced in bridging data science and production environments.
• Professionals comfortable owning infrastructure reliability and performance outcomes.

This Role Is Not For...

• Candidates focused solely on research without deployment responsibility.
• Engineers unwilling to engage with operational complexity or team leadership.