Description

You will architect and implement MLOps solutions for machine learning projects.

Responsibilities

• Design and optimize pipelines for model deployments in production using containers (Docker or Azure Kubernetes), Azure DevOps, and/or MLOps and Azure Data Factory.
• Implement efficient microservices frameworks, including API Management, message brokers, and load balancing.
• Troubleshoot, improve, and scale continuous integration, continuous delivery, and continuous deployment (CI/CD) pipelines.
• Write design documents to build consensus for new systems components and enhancements to existing components.
• Demonstrate a history of designing solution pipelines from conception to deployment in production environments using Docker containers on Kubernetes-based platforms with data orchestration in Azure Data Factory.

Required Skills

• 12-15 years of experience supporting machine learning projects.
• Expert proficiency with ML platforms such as TensorFlow and PyTorch.
• Proven experience with cloud platforms, specifically Azure.
• Strong programming skills in Python and/or Java, with experience in Object-Oriented Programming.
• Experience with big data tools like Hadoop and Spark, and databases including SQL and NoSQL.
• Mandatory knowledge of Azure Databricks pipeline and ML Model deployment.
• Experience with CI/CD pipelines, Automated Testing, Automated Deployments, and Agile methodologies.
• Extensive experience in Azure DevOps & Azure Cloud Services (e.g., Azure Blob, Azure Key Vault, Azure Data Factory).
• Bachelor's degree in Computer Science, Engineering, or a related field.

Key Skills

Education

Any Graduate