ML Ops Engineer

Job Title:

MLE-ML OPS Engineer

Roles & Responsibilities:

You Will:

• Focus on ML model load testing and creation of E2E test cases
• Evaluate models scalability and latency by running suites of metrics under different RPS and creating and automating the test cases for individual models, ensuring a smooth rollout of the models
• Enhance monitoring of model scalability, and handle incident of increased error rate
• Collaborate with existing machine learning engineers, backend engineers and QA test engineers from cross-functional teams
• You Bring:
• Advanced degree (Master’s or Ph.D.) in Computer Science/Statistics/Data Science, specializing in machine learning
• 3+ years of industry experience (pls don’t include years in a research group or R&D team)
• Strong programming skills in languages such as Java, Python, Scala
• Hands-on Experience in Databricks, mlFlow, Seldon
• Excellent problem-solving skills and analytical skills
• Expertise in recommendation algorithms
• Experience with software engineering principles, and use of cloud services like AWS
• Preferred Qualifications:
• Experience in Kubeflow, Tecton, Jenkins
• Experience in building and monitoring large-scale online customer-facing ML applications, preferably recommendation systems
• Experience working with custom ML platforms, feature store, and monitoring ML models
• Familiarity with best practices in machine learning and software engineering