Senior MLOps Engineer

What you'll do

• Build and scale ETL pipelines and deploy models into customer-facing applications.
• Design and maintain scalable infrastructure for model serving, training environments, and orchestration systems.
• Develop reusable frameworks and standardized tools to streamline machine learning implementation.
• Maintain monitoring, alerting, and automated testing frameworks for data pipelines and models.
• Manage and communicate cloud infrastructure costs and budgets to project stakeholders.
• Document and communicate best practices across the entire data science lifecycle.
• Provide technical expertise to data scientists for using cloud-based tools and infrastructure.

What we're looking for

• Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics or equivalent quantitative field.
• Master’s Degree (preferred).
• 3+ years of experience as a Machine Learning Engineer with a proven track record of successful project delivery.
• 5+ years of experience in Machine Learning (preferred).
• Experience in MLOps or DevOps practices, including Docker, Kubernetes, CI/CD pipelines, Git, API development, and model serving.
• Extensive expertise in Python and machine learning libraries like TensorFlow, PyTorch, and scikit-learn.
• Extensive knowledge of distributed computing and big data technologies like Spark, Kubeflow, Airflow, and SQL.
• In-depth knowledge of cloud platforms, preferably Google Cloud Platform services like Vertex AI, BigQuery, and Dataproc.