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Azure MLOps Architect needs 8+ years extensive experience with Azure Machine Learning (ML) and MLOps processes, including creating and optimizing ML workflows, building serving platforms, and deploying models.
Azure MLOps Architect requires:
• Azure MLOps Expertise: Extensive experience with Azure Machine Learning (ML) and MLOps processes, including creating and optimizing ML workflows, building serving platforms, and deploying models.
• ML Deployment Pipelines: Expertise in building and maintaining ML deployment pipelines in Azure, ensuring seamless model delivery to production.
• Model Experimentation: Strong background in ML model experimentation to optimize and evaluate models effectively.
• High-Performance Model Serving: In-depth experience in serving high-performance models at scale with low latency.
• MLOps Engineering & Architecture: Solid experience in MLOps engineering and designing architecture for scalable ML solutions.
Nice-to-Have Skills:
• Databricks: Experience with Databricks for data engineering and machine learning workflows.
• GCP Experience: Experience working with Google Cloud Platform (GCP) in MLOps or data engineering capacities.
• Cosmos DB (DogDB/Stardog): Knowledge of Cosmos DB, particularly with DogDB/Stardog schema, vector search, and embeddings.
• Serverless Compute & Kubernetes (AKS): Familiarity with serverless compute, Kubernetes (AKS), and containerized environments.
• Infrastructure Tools: Experience with Terraform for infrastructure as code, particularly for app development and infrastructure deployment.
• Event Hubs & API Gateways: Proficiency in designing and optimizing low-latency services, API gateways, and event hubs.
• Performance Tuning: Strong focus on performance tuning and load management for high-performance

Azure MLOps Architect duties:
• Support the development and deployment of the Azure MLOps platform to enhance PayPal's machine learning capabilities and infrastructure.
Reference : Azure MLOps Architect PAYS $75/HR San Francisco, CA jobs

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Published at 21-01-2025
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