Azure ML Solution Architect

The Azure ML Solution Architect will drive the design and implementation of our enterprise MLOps infrastructure. This role will be crucial in establishing secure, scalable, and compliant ML platforms that support our clinical research operations.

Remote based position.

Summary of Responsibilities:

• Design and implement enterprise-grade MLOps architectures on Azure, focusing on security, scalability, and regulatory compliance.

• Establish architectural patterns and best practices for ML model deployment, monitoring, and lifecycle management.

• Lead the development of ML pipelines and automated workflows using Azure ML Studio, Azure AI Foundry, and Azure Data Factory.

• Architect secure integration patterns between ML systems and clinical research platforms.

• Drive standardization of MLOps practices across development teams.

• Collaborate with data scientists and ML engineers to optimize model deployment workflows.

• Ensure compliance with GxP, HIPAA, and other relevant regulatory frameworks

• Provide technical leadership and mentoring to ML engineering teams.

• Partner with infrastructure teams to optimize Kubernetes-based ML deployment platforms.

Qualifications (Minimum Required):

• Expertise in infrastructure-as-code using tools like Terraform

• Experience with CI/CD pipelines for ML workflows

• Proficiency in Python and other ML-related programming tools

Experience (Minimum Required):

• 7+ years of experience in software architecture or infrastructure engineering

• Deep understanding of MLOps principles and practices

• Experience building and maintaining ML platforms in regulated environments

• Strong knowledge of security best practices and compliance requirements

Preferred Qualifications Include:

• 3+ years of hands-on experience with Azure MLOps cloud services, particularly:

• Azure Kubernetes Service (AKS)

• Azure ML Studio

• Azure Data Factory

• Azure AI Foundry

• Azure DevOps

• Experience in clinical research or pharmaceutical industry

• Familiarity with GxP compliance requirements

• Knowledge of distributed systems and microservices architecture

• Experience with real-time ML serving platforms

• Background in data engineering or ML model development

• Azure certifications (Azure Solutions Architect, Azure DevOps Engineer)

Physical Demands / Work Environment:

• Remote work environment with occasional travel as needed.

• Ability to work across multiple time zones with global teams.

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