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Description
MLOps Engineer sits at the intersection of Machine Learning, Software/Data Engineering, and DevOps. They integrate the best practices from each of these fields to ensure the effective deployment and management of machine learning models in production environments.
Responsibilities
MLOps Engineer sits at the intersection of Machine Learning, Software/Data Engineering, and DevOps. They integrate the best practices from each of these fields to ensure the effective deployment and management of machine learning models in production environments.
Responsibilities
- Technical Leadership: Ability to recommend and advocate for MLOps design patterns, best practices, and tooling in an enterprise setting. Ability to lead the implementation of the same.
- Technical Debt Resolution: Address and resolve technical debt in current ML projects in production and incorporate best MLOps practices.
- Model Deployment: Deploy machine learning models and enhance automation in the deployment process.
- Automation and Checks: Implement and manage automation pipeline, set up necessary tests for continuous integration and deployment.
- Monitoring and Maintenance: Monitor data drift, model performance and other metrics in production and work with Data Scientists to retrain models and set up retraining pipelines.
- Security & Compliance: Ensure that ML systems comply with security standards and best practices in a cloud environment.
- Collaboration: Work closely with:
- Data Engineers and data modellers to understand the data pipelines and data models.
- Data scientists to understand experimental ML models and ensure that models are integrated and operationalized effectively.
- Software engineers/IT to deploy infrastructure.
- 7-10 years of overall experience in software engineering, data engineering, or MLOps preferably with enterprise-level, complex matrix organizations.
- Experience in setting up MLOps pipelines, systems and processes from scratch.
- Proven experience with AWS (Athena, Glue, ECS, EKS, VPC, etc.) and AWS SageMaker specifically for deploying machine learning models, enhancing automation and implementing necessary checks for continuous improvements.
- Develop and manage CI/CD pipelines (Azure Pipelines preferred) to automate model deployment, testing, and integration processes.
- Orchestration and monitoring of data pipelines and ML workflows, ensuring timely execution and monitoring (Apache Airflow preferred).
- Strong experience with Python and Bash for automating ML workflows, SQL and Pyspark for feature engineering.
- Familiarity with IaC tools such as Terraform or AWS CloudFormation for managing cloud infrastructure.
- Knowledge of security practices and compliance requirements for managing data and models in the cloud.
- Knowledge of Data Science/Machine Learning lifecycle and frameworks such as scikit-learn, Pytorch, Tensorflow.
- Experience with Azure Synapse and Azure ML Studio
- Experience with Databricks
- Experience with dbt
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