Description

You will design, implement, and manage scalable machine learning pipelines and deployment infrastructure.

This role is on-site.

Responsibilities

• Build and maintain automated CI/CD pipelines using GitHub, GitHub Actions, and SonarQube for code quality and security.
• Containerize and deploy machine learning models on Azure Kubernetes Service (AKS) to ensure high availability and scalability.
• Develop reusable templates for ML use cases and manage APIs for seamless model integration.
• Monitor data drift, perform data refresh checks, and optimize pipelines for cost efficiency.
• Collaborate with data scientists and DevOps teams to deploy models and document workflows.

Required Skills

• 5+ years of experience in MLOps or DevOps engineering.
• Hands-on expertise with Azure ML, Databricks, and PySpark.
• Strong proficiency in building ML pipelines and managing CI/CD processes.
• Experience with containerization and orchestration, specifically Azure Kubernetes Service (AKS).
• Solid understanding of API design and integration.
• Ability to design scalable architectures and implement cost-monitoring strategies.
• Bachelor’s degree in any discipline.

Preferred Skills

• Experience implementing SonarQube for code quality enforcement.
• Familiarity with data drift detection and model optimization techniques.

Key Skills

Education

Any Graduation