MLOps Engineer

Description:

This role requires a blend of skills in software engineering, machine learning, and operations to ensure the smooth functioning of ML systems in production environments. In this role you will:

• Lead the team to design and implement automation for model training, testing, validation, and deployment
• Collaborate with machine learning engineers to ensure efficient deployment and scaling of ML models
• Implement monitoring and alerting systems to track model performance, system health, and data drift
• Optimize compute resources for cost and performance efficiency
• Manage model versions to ensure traceability and reproducibility

Minimum Qualifications:

• 3 years of experience in the design and implement of Large-scale ML Systems or Distributed Systems
• Experience with model pipeline and registry tools, detecting and preventing model drift, automating model monitoring, and ensuring model accuracy
• Proficiency in programming languages such as Python, Java, or Golang
• Effective communication skills in written and spoken English
• Bachelor or above in Software Engineering, Computer Science, Machine Learning or a related field

Preferred Qualifications:

• Experience in machine learning frameworks such as TensorFlow, PyTorch, AutoGluon, XGBoost or Scikit-learn
• Experienced in DevOps Tools such as Docker, Jenkins, Ansible, Grafana, Prometheus, Elastic, or Kubernetes
• Familiar with CI/CD deployment practices
• Experience with SQL and database systems such as PostgreSQL
• Experience with building ETL pipeline in data warehouse such as Snowflake
• Experience with inference optimization