MLOps/DevOps Engineer

Core Job Responsibilities:

• Develop end-to-end pipelines encompassing the ML lifecycle from data ingestion, data transformation, model training &validation, model deployment & serving, and model evaluation over time.
• Collaborate closely with AI scientists to accelerate productionization of ML
• algorithms.
• Setup CI/CD/CT pipelines for ML algorithms.
• Deploy models as a service both to cloud and on-prem edge.
• Manage a team of DevOps/MLOps engineers.
• Learn and apply new tools, technologies, and industry best practices.

Key Qualifications

• MS in Computer Science, Software Engineering, or equivalent field
• Experience with Cloud Platforms, especially GCP, and related skills: Docker, Kubernetes, edge computing.
• Familiarity with task orchestration tools, such as MLflow, Kubeflow, Airflow, Vertex AI, Azure ML, etc
• Fluency in at least one general purpose programming language, Python preferred.
• Strong DevOps skills: Linux/Unix environment, testing, troubleshooting,
• automation, Git, dependency management, and build tools (GCP Cloud Build,
• Jenkins, Bazel, Gitlab CI/CD, Github Actions, etc).
• Data engineering skills a plus, such as Beam, Spark, Pandas, SQL, Kafka, GCP Dataflow, etc
• 5+ years of experience, including academic experience, in any of the above.
• 3+ years of managing a DevOps/MLOps team.