Vacancy expired!
As a member of the Data Ops team as an
ML Ops Engineer, you will be responsible for providing and maintaining a robust and scalable Cloud Linux-based workloads environment for machine learning in Microsoft Azure. This includes configuring and provisioning components based on end-users needs, productionizing machine learning resources and routines, training platform users and advising on best practices, and providing continued monitoring solutions for platform stability. You will support our IT and data science stakeholders in their efforts to design, deploy, and maintain machine learning models that provide value to many different areas of the business - from applications. You will collaborate with other IT teams, including architecture, network teams, and DevOps Catalog to build solutions that meet the customer's needs within our organizational parameters and processes. What You'll Be Doing- Work on a team responsible for technical delivery of an Azure Cloud Linux-based workloads environment (Python) in an agile environment
- Design and build scalable cloud infrastructure for model management, continuous training and deployment, and serving predictions
- Create and maintain pipelines in Azure DevOps to automate deployment of code and resources (CICD)
- Create and configure appropriate cloud resources to meet the needs of the end users, including Azure Kubernetes Clusters, Machine Learning Workspaces, and compute instances
- Work with platform users to understand requirements and use cases, and to advise on strategy and best practice for use of the platform
- Work with the team to identify opportunities to automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines
- Monitor cloud-based systems and components for availability, performance, reliability, security, efficiency, and ability to meet non-functional requirements and service level agreements.
- Support processes to monitor ML applications for operational and ML-related issues, compare model inputs between training and inference, explore model-specific metrics
- Work closely with data scientists, engineers, and product managers to understand platform usage and identify opportunities to create automation and self-service tools over current manual processes.
- Perform all support activities including training and enablement of platform users
- Bachelor's degree or higher in Computer Science / Computer Engineering or related field.
- AZ AI -100 and/or AZ AI 900 certification
- Experience with Azure Machine Learning
- Understanding of the Machine Learning Lifecycle and machine learning concepts
- Applied knowledge of CI/CD DevOps principles and Azure ML pipelines
- Demonstrated experience building and scaling end to end machine learning systems
- Demonstrated experience with Powershell, BASH, or shell scripting
- Strong knowledge of Linux OS and Windows servers
- Applied experience with Python for data manipulation and analysis solutions
- Exposure to containers: Docker, Kubernetes, or OpenShift
- Working Experience with Azure DevOps
- Strong experience with version control (E.g., git) within Azure DevOps
- Experience with monitoring tools (E.g., AppInsights, Splunk)
- Prior experience in applying Agile development methodology
- Experience performing Root Cause Analysis (RCA) for application and infrastructure
- Experience with SharePoint or Microsoft Business Apps and Automation (CRM, PowerApps) platforms
- Desire to obtain or utilize technical certifications as part of continuous professional growth
- Ability to learn fast, adapt to new technology and changes in the environment
- Ability to work effectively and manage multiple priorities while collaborating with internal and external cross-functional teams
- Smart people with a passion for technology
- Strong technical capabilities with a consultancy mindset
- Close involvement with local technical communities
- A willingness to think outside of the box to provide innovative solutions to clients
- Ability to solve challenging technical business problems
- Self-directed professionals
- Client Success
- C ontinued Learning and Technical Excellence
- Strong Client Relationships
- Citizenship and Community
Vacancy expired!