Senior ML Ops Engineer (Machine Learning Infrastructure)
Parallel Systemsabout 3 hours ago
Base Salary
$150k - $250k/yr
Responsibilities
- Design and implement robust MLOps solutions, including automated pipelines for data management, model training, deployment, and monitoring.
- Architect, deploy, and manage scalable ML infrastructure for distributed training and inference.
- Collaborate with ML engineers to gather requirements and develop strategies for data management, model development, and deployment.
- Build and operate cloud-based systems optimized for ML workloads in R&D and production environments.
- Support the automation of model evaluation, selection, and deployment workflows.
Requirements
- Bachelor’s or higher degree in Computer Science, Machine Learning, or a relevant engineering discipline.
- 5+ years of experience building large-scale, reliable systems; 2+ years focused on ML infrastructure or MLOps.
- Proven experience architecting and deploying production-grade ML pipelines and platforms.
- Strong knowledge of the ML lifecycle: data ingestion, model training, evaluation, packaging, and deployment.
- Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, or similar.
- Deep understanding of CI/CD practices applied to ML workflows.
- Proficiency in Python, Git, and system design with solid software engineering fundamentals.
- Experience with cloud platforms (AWS, GCP, or Azure) and designing ML architectures in those environments.