Machine Learning Systems Operations Engineer

📋 Description

• Design and maintain scalable infrastructure for model training, serving, monitoring, and feature management.
• Build and manage data pipelines, feature stores, and metadata stores to support ML workflows.
• Optimize memory and compute efficiency for large-scale training and inference.
• Enable distributed training and deployment across heterogeneous hardware (CPU, GPU, etc.).
• Automate end-to-end ML workflows using orchestration tools like Airflow or Argo.
• Ensure high availability, observability, and reliability of ML systems in production.

🎯 Requirements

• Bachelor’s or Master’s degree in CS, Engineering, or related field
• 3+ years in backend, infrastructure, or ML systems engineering
• Proficiency in Python and at least one backend language (Java/Go/Scala)
• Experience with SQL/NoSQL, feature stores, and distributed data processing (Spark/Kafka)
• Experience with cloud platforms (AWS/GCP/Azure), Docker, Kubernetes, and CI/CD for ML workflows
• Familiarity with MLOps tools (MLflow/Kubeflow) and distributed training (PyTorch)