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.