Senior MLOps Engineer

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