MLOPS Architect

Responsibilities

• Document architecture, workflows, and best practices for knowledge sharing and compliance.
• Provide technical oversight & Guidelines
• Architect and implement end-to-end MLOps and LLMOps pipelines using Azure Machine Learning and Azure OpenAI.
• Design scalable infrastructure for training, deploying, and monitoring ML and LLM models in production.
• Collaborate with data scientists and engineers to streamline model development, testing, and deployment workflows.
• Manage Azure Kubernetes Service (AKS) clusters and containerized ML workloads.
• Ensure model governance, versioning, and reproducibility using tools like MLflow and Azure DevOps.
• Promote DevSecOps practices, ensuring security and compliance are embedded in the ML lifecycle.
• Monitor and troubleshoot production ML systems, ensuring high availability and performance.

Qualifications

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