- Design, implement, and manage scalable machine learning (ML) pipelines using Azure ML, Databricks, and PySpark.

- Build and maintain automated CI/CD pipelines with Github and Github Action, 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.

- Have understanding of over all architecture and can work on scalable solutions.

- 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, monitor 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.