ML Ops Engineer

As a Machine Learning Operations (ML Ops) Engineer, you will play a pivotal role in bridging the gap between data science and IT operations. Your primary responsibility will be to streamline and operationalize the end-to-end machine learning lifecycle, ensuring the smooth deployment, monitoring, and maintenance of machine learning models in production environments.

• Deployment and Integration
  • Deploy machine learning models into production environments, collaborating with data scientists and software engineers to integrate models into existing systems.
  • Develop and maintain deployment pipelines for machine learning models, utilizing tools like Docker, Kubernetes, and other containerization technologies.
  • Utilize Amazon SageMaker for seamless model deployment and management, taking advantage of its scalable infrastructure and built-in tools for model training and hosting.
  • Develop and maintain deployment pipelines for machine learning models, integrating with MLflow for experiment tracking and model versioning.
• Automation and Orchestration:
  • Implement automation scripts and tools to facilitate seamless model deployment, monitoring, and scaling.
  • Orchestrate the workflow between data science, development, and operations teams to optimize the end-to-end ML lifecycle.
• Infrastructure Management:
  • Manage the infrastructure required for machine learning model deployment, including cloud resources and on-premises servers.
  • Optimize infrastructure for scalability, performance, and cost-effectiveness.
  • Utilize Data Version Control (DVC) to manage and version machine learning data, ensuring reproducibility and traceability.
  • Utilize AWS services, including SageMaker, for efficient model hosting and management.
• Monitoring and Maintenance:
  • Establish monitoring systems to track model performance, resource utilization, and other relevant metrics.
  • Monitor and address model drift, implementing strategies for detecting and mitigating changes in data patterns that impact model accuracy.
  • Collaborate with data scientists to develop strategies for model retraining and versioning to address model drift.
  • Leverage MLflow for experiment tracking and model versioning, ensuring reproducibility and traceability.
• Security and Compliance:
  • Implement security best practices for machine learning systems, ensuring the confidentiality and integrity of data and models.
  • Ensure compliance with relevant regulations and standards in the deployment and maintenance of machine learning models.
• Collaboration and Communication:
  • Work closely with cross-functional teams, including data scientists, software engineers, and operations, to foster a collaborative and efficient working environment.
  • Clearly communicate technical concepts and requirements to non-technical stakeholders.

Bachelor’s or master’s degree in computer science, Machine Learning, or a related field.

• 3-5 years of proven experience in deploying and managing machine learning models in production environments.
• Strong proficiency in programming languages such as Python, and experience with relevant frameworks (e.g., TensorFlow, PyTorch).
• Familiarity with containerization tools (Docker, Kubernetes) and cloud platforms (AWS, Azure).
• Solid understanding of DevOps principles and practices.
• Experience with version control systems (Git) and continuous integration/continuous deployment (CI/CD) pipelines.
• Knowledge of security best practices in machine learning deployments.
• Experience in addressing and mitigating model drift.
• Familiarity with Data Version Control (DVC) for managing and versioning machine learning data.
• Proficiency in Amazon SageMaker and MLflow for model deployment, management, and experiment tracking.
• Excellent communication and collaboration skills.