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 Computer Science, Engineering, or related field.
• 3+ years in backend, infrastructure, or ML systems engineering.
• Proficiency in Python and at least one backend language (Java, Go, or Scala).
• Hands-on experience with SQL/NoSQL databases, feature stores, Spark, Kafka.
• Experience with cloud platforms (AWS, GCP, or Azure), Docker, Kubernetes, and CI/CD pipelines for ML workflows.
• Familiarity with MLOps tools such as MLflow or Kubeflow, and distributed training frameworks (PyTorch).