Senior MLOps Engineer

Job Summary

Owning the operational foundation for ranking and recommendation models, the full-time Senior MLOps Engineer will manage the complete model lifecycle, build scalable ML data pipelines, and ensure reliable production systems in a remote work environment.

Key responsibilities

• Own the complete model lifecycle from experimentation through validation, deployment, versioning, retraining, rollback, and retirement
• Design and build scalable ML data and feature engineering pipelines using Spark/PySpark and modern lakehouse technologies
• Build monitoring for data quality, drift, prediction quality, service reliability, latency, and infrastructure cost

Required qualifications

• 5+ years of experience building and operating production ML systems, including monitoring and continuous improvement
• Strong production software development skills in Python and proficiency with SQL
• Hands-on experience with Spark/PySpark for large-scale distributed data processing
• Experience with modern data lake or lakehouse architectures, including S3 and Apache Iceberg
• Experience deploying and operating containerized production workloads on Kubernetes