Senior ML Ops Engineer (Machine Learning Infrastructure)

📋 Description

• Design and implement MLOps pipelines for data, training, deployment, and monitoring.
• Architect and manage scalable ML infra for distributed training and inference.
• Collaborate with ML engineers to align data, model development, and deployment strategies.
• Build cloud-based ML systems (AWS, GCP) for R&D and production workloads.
• Create scalable infra to enable CI/CD, experiment management, and governance of models and datasets.
• Support automation of model evaluation, deployment workflows.

🎯 Requirements

• Bachelor’s or higher in CS, ML, or related engineering field.
• 5+ years building large-scale reliable systems; 2+ years ML infra or MLOps.
• Proven experience architecting and deploying production-grade ML pipelines.
• Strong knowledge of ML lifecycle: data ingestion, training, evaluation, packaging, deployment.
• Hands-on with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow).
• Proficiency in Python, Git, and cloud platforms (AWS/GCP/Azure).

🎁 Benefits

• Hybrid work with on-site in Los Angeles (min 1 week/month).
• Equal opportunity employer and commitment to diversity.