Posted 2 years ago


  • Design and build effective, user-friendly infrastructure, tooling, and automation to accelerate Machine Learning 
  • Collaborate with teams to drive the ML technical roadmap
  • Collaborate with Machine Learning Engineers and Product Managers to develop tools to support experimentation, training and production operations
  • Build and maintain data pipelines using tools like Hadoop, Python, Airflow, and Kafka
  • Offer support and troubleshooting assistance for the ML pipeline, while continuously improving stability along the way
  • Build and maintain systems employing an Infrastructure-as-Code approach
  • Own the AWS stack which comprises all ML resources
  • Establish standards and practices around MLOps, including governance, compliance, and data security
  • Collaborate on managing ML infrastructure costs

Skills And Qualifications

  • 3+ years of experience with ML infrastructure and ML DevOps
  • 5+ years of overall engineering experience in distributed systems and data infrastructure
  • 3+ years’ experience coding in Python (preferred) or other languages like Java, C#, Golang etc. 
  • Experience working with ML engineers to build tooling and automation to support the entire ML engineering lifecycle, from experimentation to production operations
  • Experience with Kubernetes and ML CI/CD workflows
  • 3+ years’ experience with AWS or other public cloud platforms (GCP, Azure, etc.)
  • Excellent verbal and written communication skills.

Job Features

Job Category

ML Op’s

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