ML OPS Engineer

This role calls for collaboration with various professionals such as data scientists, machine learning engineers, data engineers, software engineers, and platform architects.

Responsibilities:

• Develop automated build and deployment procedures to continuously deliver software releases and enhance existing CI/CD pipelines for AIML application development and deployment.
• Collaborate with a team of data scientists, data engineers, data analysts, software engineers, IT specialists, and stakeholders to expedite the deployment of AI applications via CI/CD pipelines and maintain the SLAs of these applications.
• Design, develop, and sustain infrastructure using infrastructure as code tools such as Terraform, Ansible, CloudFormation, etc.
• Work on templatizing existing Databricks CLI codes to manage Databricks platform as code for AIML data pipelines (batch processing, batch streaming, and streaming) and model serving endpoints.
• Improve existing DevOps practices to enhance the overall AIML application development lifecycle.
• Collaborate with development teams and the cloud platform team to ensure that the infrastructure meets the application's requirements.
• Establish and maintain best practices for cloud security, compliance, and cost optimization.
• Ensure that applications are highly available and scalable by working closely with cross-functional teams.
• Adopt event-driven and microservice architectures for enterprise-level platform development.
• Containerize analytical models using Docker and Kubernetes or other container orchestration platforms. • Minimum of 5 years of experience in a relevant field.
• Proven expertise in Databricks.
• Proficiency in using Amazon Web Services (AWS).
• Solid experience with Amazon Machine Learning.
• Demonstrable skills in Machine Learning.
• Prior experience in Machine Learning Engineering.
• Familiarity with Machine Learning Libraries.
• A background in the automotive industry is desirable.