Machine Learning Operations Engineer - Intermediate

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Requirements:

• 2-4 years of experience in MLOps, machine learning engineering, or related fields, with hands-on experience deploying and maintaining ML models in production.
• A degree in Computer Science, Engineering, or a related field (Masters level or higher is preferred).
• Demonstrable understanding of machine learning and AI principles, models, tools, and their applications.
• Strong SQL knowledge for data retrieval.
• Proficiency in Python for data extraction and manipulation.
• Solid working knowledge of AWS systems and services, including SageMaker, EC2, S3, and Lambda.
• Experience with MLOps, versioning, orchestration, and containerization tools such as MLFlow, Kubeflow, Airflow, Docker, and Kubernetes.
• Strong understanding of statistical analysis methods.
• Excellent analytical and problem-solving skills.

Responsibilities:

• Develop key data pipelines to manage data for ML and AI models in production.
• Support a robust ML-Ops framework for model tracking and continuous improvement.
• Automate statistical analysis of production ML models.
• Collaborate with teams to identify and resolve potential performance and availability issues.
• Explore innovative solutions for complex data challenges.
• Manage multiple projects and support the Machine Learning team in building data products.