Lead AI Security Architect - Azure & Databricks

Molina Healthcare; Long Beach (CA); Remote; USD 112.000 - 218.000; Vollzeit

Zusammenfassung

Molina Healthcare seeks a senior cybersecurity architect to define and evolve secure AI/ML architectures across Azure, Databricks, and other platforms. You will lead threat modeling, risk assessments, and security validation for AI models, data, and integrations.

You will collaborate with cross‑functional teams to embed security controls, guide implementation, and ensure resilient, compliant AI deployments across enterprise use cases.

Qualifikationen

• 5+ years in a senior cybersecurity role or equivalent.
• Hands-on security of AI/ML workloads on Azure and Databricks.
• Translate AI threat models into reusable security patterns and tests.
• Understand AI/ML lifecycle and MLOps with security in mind.
• Knowledge of cloud-native security controls and architectures.
• Familiarity with AI risks like prompt injection and data poisoning.
• Proficiency in Python for security testing and automation.
• Strong communication to technical and non-technical stakeholders.

Aufgaben

• Defines secure AI/ML architecture patterns for Azure/Databricks.
• Defines lifecycle security requirements and evaluation processes for AI models.
• Performs threat modeling and risk assessments for AI/ML systems.
• Leads security for autonomous AI agents and constrains behaviors.
• Sets trust boundaries, access controls, and data protection for AI tools.

Kenntnisse

AI/ML security

Security architecture

Threat modeling

Python for security testing

Communication

Leadership

Security policy

Tools

Databricks

Microsoft Azure

Model Context Protocol (MCP)

CI/CD tooling

Jobbeschreibung

Molina Healthcare seeks a senior cybersecurity architect to define and evolve secure AI/ML architectures across Azure, Databricks, and other platforms. You will lead threat modeling, risk assessments, and security validation for AI models, data, and integrations.

You will collaborate with cross‑functional teams to embed security controls, guide implementation, and ensure resilient, compliant AI deployments across enterprise use cases.