ML Ops Lead

Introduction:

The ML Ops Lead will play a crucial role in overseeing the design, implementation, and maintenance of ML Ops solutions using AWS services like Amazon SageMaker. They will be responsible for building out an evolving ML / AI platform on the cloud, while also managing a geographically distributed team and collaborating with key internal stakeholders.

Responsibilities:

• Design, implement, and maintain ML Ops solutions using AWS services like Amazon SageMaker
• Manage relationships with key internal stakeholders and drive product roadmap
• Collaborate with cross-functional teams including data scientists, data engineers, software developers, and business stakeholders
• Influence and collaborate with various stakeholders including platform users, architects, cloud engineering, DevOps, and others
• Lead and manage a geographically distributed team of talented engineers, providing technical directions, coaching junior members, and managing priorities

Requirements:

Required Skills:

• Proven experience in end-to-end lifecycle of ML Ops / GenAI platform build out in cloud (AWS preferred)
• Several years of experience in technical product management or cloud platform product leadership
• Hands-on experience with cloud infrastructure, preferably AWS, and strong working knowledge of AWS services such as SageMaker, ECS, IAM, CloudWatch, S3, etc.
• Proficient in GitHub workflows, CI/CD, and version control practices
• Proficient in Python and its ecosystem of tooling such as Jupyter Notebooks, Packaging, Documentation

Preferred Skills:

• Familiarity with Infra-as-code such as AWS CDK