Job Description

Key Responsibilities
• At least 4 to 6 years of relevant experience in MLOps team with 2-3 years hands-on
experience in supporting Azure MLOPS cloud technologies
• Design, implement, and manage scalable machine learning (ML) pipelines using
Azure ML, Databricks, and PySpark.
• Build and maintain automated CI/CD pipelines with Bitbucket and Jenkins,
incorporating SonarQube to ensure code quality and security standards.
• Utilize Azure Kubernetes Service (AKS) to containerize and deploy machine learning
models, ensuring high availability and scalability.
• Develop reusable templates for various ML use cases to streamline the model
deployment process and enhance operational efficiency.
• Design and manage APIs to facilitate seamless interaction between ML models and
other applications, ensuring robust, secure, and scalable API interfaces.
• Perform model optimization, monitoring for data drift, data refresh checks, and
ensure the ML pipelines are cost-efficient.
• Implement cost monitoring and management strategies to ensure efficient use of
resources, particularly for model training and deployment phases.
• Work closely with data scientists, DevOps, and IT teams to deploy and manage
machine learning models across environments.
• Provide thorough documentation for ML workflows, pipeline templates, and
optimization strategies to support cross-team collaboration