Engineer III - ML Platform (Remote, IND)

View full description

Requirements:

• 3+ years experience developing and deploying machine learning solutions to production. Familiarity with typical machine learning algorithms from an engineering perspective (how they are built and used, not necessarily the theory); familiarity with supervised / unsupervised approaches: how, why, and when and labeled data is created and used
• 3+ years experience with ML Platform tools like Jupyter Notebooks, NVidia Workbench, MLFlow, Ray, Vertex AI etc.
• Experience building data platform product(s) or features with (one of) Apache Spark, Flink or comparable tools in GCP. Experience with Iceberg is highly desirable.
• Proficiency in distributed computing and orchestration technologies (Kubernetes, Airflow, etc.)
• Production experience with infrastructure-as-code tools such as Terraform, FluxCD
• Expert level experience with Python; Java/Scala exposure is recommended. Ability to write Python interfaces to provide standardized and simplified interfaces for data scientists to utilize internal Crowdstrike tools
• Expert level experience with CI/CD frameworks such as GitHub Actions
• Expert level experience with containerization frameworks
• Strong analytical and problem solving skills, capable of working in a dynamic environment
• Exceptional interpersonal and communication skills. Work with stakeholders across multiple teams and synthesize their needs into software interfaces and processes.

Responsibilities:

• Help design, build, and facilitate adoption of a modern Data+ML platform
• Modularize complex ML code into standardized and repeatable components
• Establish and facilitate adoption of repeatable patterns for model development, deployment, and monitoring
• Build a platform that scales to thousands of users and offers self-service capability to build ML experimentation pipelines
• Leverage workflow orchestration tools to deploy efficient and scalable execution of complex data and ML pipelines
• Review code changes from data scientists and champion software development best practices
• Leverage cloud services like Kubernetes, blob storage, and queues in our cloud first environment