Machine Learning Engineer

• Model Deployment: Manage and optimize model deployment processes, including the use of Kubernetes for containerized model deployment and orchestration
• Model Registry Management: Maintain and manage a model registry to track versions and ensure smooth transitions from development to production
• Design, develop, and maintain robust ETL/ELT, curated and feature engineering processes using Python and SQL to extract, transform, and load data from various sources into our data platforms
• CI/CD Implementation: Develop and implement Continuous Integration/Continuous Deployment (CI/CD) pipelines for model training, testing, and deployment, ensuring high code quality through rigorous model code reviews
• Model Monitoring & Optimization: Design and implement model inference pipelines and monitoring frameworks to support thousands of models across various pods, optimizing execution times and resource usage
• Collaboration with Data Science Teams: Train and collaborate with data science team members on best practices in tools such as Kubeflow, Jenkins, Docker, and Kubernetes to ensure smooth model productionization
• Reusable Frameworks Development: Draft designs and apply reusable frameworks for drift detection, live inference, and API integration
• Cost Optimization Initiatives: Propose and implement strategies to reduce operational costs, including optimizing models for resource efficiency, resulting in significant annual savings
• Documentation & Standards Development: Produce MLE standards documents to assist data science teams in deploying their models effectively and consistently

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field
• 4+ years of experience in data engineering or a related field, with a strong focus on Python, SQL, and Azure Cloud technologies
• Proficiency in advanced Python for model deployment, data manipulation, automation, and scripting
• Proficient in Kubernetes, model monitoring, and CI/CD practices
• Productionizing machine learning models, Experience with programming languages and ML frameworks (e.g., TensorFlow, PyTorch)
• Advanced SQL skills for complex query writing, optimization, and database management
• Experience with big data technologies (e.g., Spark, Hadoop) and data lake architectures
• Familiarity with CI/CD pipelines, version control (Git), and containerization (Docker), Airflow is a plus
• Strong problem-solving and analytical skills
• Excellent communication and collaboration abilities
• Ability to work independently and as part of a team in a fast-paced environment

Familiarity with Airflow