Machine Learning Ops Engineer 2

Key Responsibilities:

Model Deployment Automation: Develop, deploy, and manage ML models on Databricks using MLflow for tracking experiments, managing models, and registering them in a centralized repository.

Infrastructure Environment Management: Set up scalable and fault-tolerant infrastructure to support model training and inference in cloud environments such as AWS, GCP, or Azure.

Monitoring Performance Optimization: Implement monitoring systems to track model performance, accuracy, and drift over time. Create automated systems for re-training and continuous learning to maintain optimal performance.

Data Pipeline Integration: Collaborate with the data engineering team to integrate model pipelines with real-time and batch data processing frameworks, ensuring seamless data flow for training and inference.

Skillset Qualification

Model Deployment: Experience with deploying models in production using cloud platforms like AWS Sagemaker, GCP AI Platform, or Azure ML Studio.

Version Control Automation: Experience with MLOps tools such as MLflow, Kubeflow, or Airflow to automate and monitor the lifecycle of machine learning models.

Cloud Expertise: Experience with cloud-based machine learning services on AWS, Google Cloud, or Azure, ensuring that models are scalable and efficient.

Engineers must be skilled in measuring and optimizing model performance through metrics like AUC, precision, recall, and F1-score, ensuring that models are robust and reliable in production settings.

Education: Bachelor s or Master s degree in Data Science, Statistics, Mathematics, or a related technical field.