Senior Azure Artificial Intelligence Security Architect - Remote

Molina Healthcare•Long Beach, CA

9d•$111,893 - $218,192•Remote

About The Position

Defines and maintains Molina’s security architecture framework for artificial intelligence and machine learning systems, evolving it as capabilities, deployment approaches, and threats change. Creates reusable security patterns, implementation guidance, and technical requirements for secure AI adoption across enterprise platforms and business use cases. Serves as the AI/ML security subject matter expert, including for Microsoft Azure and Databricks workloads, and partners with technology teams to implement and validate controls. The role designs secure, resilient, and scalable AI/ML architectures; defines lifecycle security requirements and evaluation processes; conducts AI/ML threat modeling and risk assessments; and leads security architecture for autonomous AI agents and AI tools and integrations. It works with MLOps and Data Science teams to embed security controls throughout the machine-learning lifecycle, maintains AI reference architectures and guidance, advises on alignment with business strategy, and collaborates with architecture, technology, business, governance, cybersecurity, compliance, and privacy teams. It also evaluates enterprise AI platforms and services, tracks emerging threats, and develops security capabilities that support AI governance and observability.

Requirements

At least 5 years of experience in a senior cybersecurity role, such as Security Architect or Cloud Security Engineer, or an equivalent combination of relevant education and experience.
Hands-on experience securing AI/ML workloads and data pipelines within Microsoft Azure and Databricks.
Experience translating AI threat models into reusable security architecture patterns, implementation guidance, and testable technical requirements; ability to validate practical controls through prototypes or security testing and guide engineering teams in adopting them across multiple AI implementation types.
Deep understanding of the AI/ML lifecycle and MLOps practices, including where security controls and validation belong from data ingestion through model deployment.
Strong understanding of network, application, and data security controls in a cloud-native environment.
Knowledge of security challenges, threat landscapes, and mitigation approaches for AI/ML systems, including generative and agentic AI; ability to assess risks such as prompt injection, data poisoning, model inversion, adversarial attacks, tool abuse, and uncontrolled agent behavior.
Proficiency in Python for security testing, control-validation prototypes, and automation, with familiarity with data science and AI libraries.
Excellent communication skills and ability to explain complex technical concepts to technical and non-technical stakeholders.
Intellectual curiosity, integrity, and motivation to achieve long-term business outcomes.
Ability to work effectively in a team environment and lead cross-functional teams.

Nice To Haves

Practical experience applying agentic AI security architecture, implementation requirements, and control validation approaches to autonomous systems, including assessing autonomous-behavior risks and mitigating common attack vectors.
Experience with Model Context Protocol (MCP) and securing its supporting infrastructure.
Familiarity with applying MLSecOps principles through security-testing and automation tools in CI/CD pipelines.
Professional certifications such as CISSP, CCSP, or Azure Security Engineer Associate/Expert.
Experience translating governance, compliance, security, and privacy management requirements into security architecture.
Familiarity with HIPAA and HITECH requirements and ability to partner with privacy, legal, and compliance teams on AI data-handling controls, design decisions, and validation evidence.
Experience supporting agile teams with guidance on design, opportunities, impact, risks, and technical and architectural debt.
Experience designing generative AI solutions and applying machine-learning operations practices to AI models, applications, and supporting platforms.
Ability to assess the architecture and security implications of unfamiliar AI technologies and apply transferable security principles as implementation approaches evolve.
Ability to balance long-term and short-term implications of individual decisions.

Responsibilities

Develop and promote secure, resilient, and scalable architecture patterns for AI/ML solutions on Microsoft Azure, Databricks, and other platforms.
Define AI model lifecycle security requirements, architecture patterns, and evaluation processes; guide teams in assessing risks, implementing safeguards, and validating security before deployment and during operations.
Conduct AI/ML threat modeling and risk assessments, identifying vulnerabilities such as model inversion, data poisoning, adversarial attacks, and prompt injection.
Lead threat modeling, risk assessment, and security architecture for autonomous AI agents; define requirements and patterns to constrain agent actions, protect data and connected resources, and establish oversight and accountability.
Define trust boundaries, access controls, data protection, oversight, and reusable security patterns for AI tools, services, and integrations, including Model Context Protocol (MCP); partner with implementation teams to validate controls.
Collaborate with MLOps and Data Science teams to embed automated security controls throughout the machine-learning development lifecycle, from data ingestion to model deployment.
Maintain AI reference architectures and publish security guidance, standards, and patterns addressing AI technology, development, security, privacy, and observability.
Advise on AI capabilities across business and technology platforms and provide recommendations on solution design, risk mitigation, and cross-domain impacts.
Partner with solution architects, technology leaders, business, governance, cybersecurity, compliance, and privacy teams to identify AI risks, document design decisions, and provide enterprise patterns to resolve issues.
Evaluate enterprise AI platforms, products, and services against security requirements; assess capabilities, integration risks, and control gaps; define safeguards, acceptance criteria, requirements, and deployment guidance.
Track emerging threats and vulnerabilities targeting AI systems and develop proactive defensive strategies and security capabilities supporting AI governance and observability.
Update architecture requirements and guidance as model technologies, deployment approaches, integration technologies, platform capabilities, business uses, and threats evolve.