Staff ML Ops Engineer - Recommendations

What you'll do

• Own end-to-end model deployment pipeline including validation, canary rollout, and rollback strategies.
• Build automated offline evaluation pipelines and integrate them with online A/B testing frameworks.
• Manage data preprocessing workflows for training models, ensuring data quality and freshness.
• Define and maintain service level objectives (SLOs) for model serving latency and training throughput.
• Mentor engineers on operational best practices and contribute to hiring by conducting technical interviews.

What we're looking for

• 7+ years in software engineering with at least 5 years focused on MLOps or production ML systems.
• Extensive experience with ML deployment pipelines including model export, validation, canary releases, and rollback strategies.
• Solid Python skills and proficiency with workflow orchestration tools like Airflow for ML processes.
• Proven ability to build and maintain production monitoring systems with alerting, dashboards, and SLO frameworks.
• Demonstrated track record of technical leadership in driving operational strategy and influencing engineering direction beyond immediate team.
• Experience managing data pipelines at scale including batch processing, feature engineering, and ensuring data quality.