Azure MLOps Engineer - Data Modeling (9-12 yrs)

Job Description :

Required skills :

- 10+ years of professional experience in building applications using cloud services.

- Prior experience in building Machine Learning platforms using cloud services.

Cloud expertise :

- Deep knowledge of cloud platforms like AWS, Google Cloud Platform, or Azure, including their machine learning and data services (Azure preferred).

DevOps skills :

- Experience with CI/CD pipelines, infrastructure as code, and containerization technologies like Docker and Kubernetes.

Machine learning knowledge :

- Understanding of ML workflows, model training, and deployment processes.

Data engineering :

- Familiarity with data pipelines, ETL processes, and data storage solutions.

Software engineering :

- Strong programming skills, particularly in languages commonly used in ML like Python.

System design :

- Ability to architect scalable, reliable systems that integrate various services.

Automation :

- Expertise in automating workflows and processes across the ML lifecycle.

Security and compliance :

- Knowledge of best practices for securing ML pipelines and ensuring regulatory compliance.

Monitoring and logging :

- Experience setting up monitoring and logging for ML systems.

Collaboration :

- Ability to work with data scientists, software engineers, and other stakeholders.

Roles & responsibilities :

- Evaluate and select appropriate cloud services for each stage of the ML lifecycle

- Design and implement the overall architecture of the MLOps platform

- Set up automated pipelines for data preparation, model training, and deployment

- Implement version control for code, data, and models

- Ensure the platform is scalable, secure, and compliant with relevant regulations

- Provide tools and interfaces for data scientists to easily leverage the platform

- Continuously optimize the platform for performance and cost-efficiency

- This role is crucial in bridging the gap between data science and operations, enabling organizations to efficiently develop, deploy, and maintain machine learning models at scale.