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.
· 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.
