MLOps Engineer

Major Responsibilities

• Lead the enhancement of the AI platform to improve the developer experience for data and ML engineers.
• Optimize workflows by integrating state-of-the-art tools and technologies, ensuring scalability and efficiency.
• Architect and manage the cloud infrastructure supporting the MLOps platform, leveraging infrastructure-as-code (IaC) tools like Terraform.
• Optimize for scalability, security, cost-effectiveness, and high availability.
• Partner with cross-functional teams, including data scientists, product managers, and cloud engineers, to align platform development with business objectives.
• Collaborate with the AI/ML reliability engineering team to design and implement components that ensure the platform's operational reliability, observability, and fault tolerance.
• Build and maintain robust DevOps pipelines tailored for ML workflows, enabling automated model training, testing, deployment, and monitoring.
• Design and manage tools to enhance platform reliability, including dashboards, logging systems, and alerting frameworks, to ensure seamless operations.

Key Responsibilities

• Knowledge, Skills, Abilities, Behaviors Required: Service and Quality Excellence, Honor our Mission and Values, Effective Decision Making, Attain and Leverage Strategic Relationships, Lead and Develop Others, Communicate with Impact, Achieve Success through Change.

Mandatory Skills

• Lead the enhancement of the AI platform to improve the developer experience for data and ML engineers.
• Optimize workflows by integrating state-of-the-art tools and technologies, ensuring scalability and efficiency.
• Architect and manage the cloud infrastructure supporting the MLOps platform, leveraging infrastructure-as-code (IaC) tools like Terraform.
• Optimize for scalability, security, cost-effectiveness, and high availability.