Machine Learning Operations Lead
About The Position
Develop a comprehensive data and analytics cloud migration strategy as part of a broader technology modernization and migration to Microsoft Azure. Drive thought leadership and execution of cross-functional analytics teams to enable adoption of data processing and analytical infrastructure in the cloud. Manage, optimize, and operationalize data lakes, data science virtual machines and other DevOps tools (Github, Jfrog) to enable faster go-to-market capabilities for data science teams. Architect and build core components of machine learning and data engineering platform infrastructure. Develop a comprehensive user, developer, manager education program to accelerate onboarding into a governed self-service data ecosystem. Define, manage and report out operational SLAs and KPIs for data platforms and solutions. Partner with data science and IT engineering teams to high performance, efficient feature pipelines from backend proprietary data. Simplify the technology stack to sunset legacy applications while minimizing business disruptions. Perform other duties as needed.
Requirements
- Bachelor’s degree (or foreign equivalent) in Applied Computer Science, Computer Engineering, Information Systems, or a related field required.
- 5 years of experience in job offered or related occupations required.
- 5 years of experience: developing architectural design documents to enable cloud transformation and migration of key on-prem services to cloud to enable faster go-to-market for engineering teams.
- 5 years of experience: with key technologies including Azure Fundamentals, Azure Databricks, Azure Data Lake storage and Azure Compute to design, develop the right set of tools for analytics research and development in Azure.
- 5 years of experience: developing orchestration pipelines for data ingest, data transfer and developing automated data pipelines to improve operational efficiency for batch file transfers across disparate systems.
- 2 years of experience: building custom applications for data and server compute to enable machine learning (ML) enabled computes to be readily available for model development and deployment for analytics teams.
- 2 years of experience: supporting a secure, robust and resilient data and cloud related services to protect systems from security vulnerabilities, enabling scaling of services based on project needs and develop backup systems to ensure redundancy of services for non-production and production workloads.
Responsibilities
- Develop a comprehensive data and analytics cloud migration strategy as part of a broader technology modernization and migration to Microsoft Azure.
- Drive thought leadership and execution of cross-functional analytics teams to enable adoption of data processing and analytical infrastructure in the cloud.
- Manage, optimize, and operationalize data lakes, data science virtual machines and other DevOps tools (Github, Jfrog) to enable faster go-to-market capabilities for data science teams.
- Architect and build core components of machine learning and data engineering platform infrastructure.
- Develop a comprehensive user, developer, manager education program to accelerate onboarding into a governed self-service data ecosystem.
- Define, manage and report out operational SLAs and KPIs for data platforms and solutions.
- Partner with data science and IT engineering teams to high performance, efficient feature pipelines from backend proprietary data.
- Simplify the technology stack to sunset legacy applications while minimizing business disruptions.
- Perform other duties as needed.
Benefits
- standard company benefits
- country specific benefits
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What This Job Offers
Job Type
Full-time
Career Level
Senior
