Senior Azure Artificial Intelligence Security Architect

Molina Healthcare•Long Beach, CA

About The Position

The Senior Azure Artificial Intelligence Security Architect defines and maintains Molina’s security architecture framework for artificial intelligence and machine learning systems, adapting it as capabilities, deployment approaches, and threats change. The role creates reusable security patterns, implementation guidance, and technical requirements to enable secure AI adoption across enterprise platforms and business use cases. It serves as a subject matter expert in AI/ML security, including Microsoft Azure and Databricks workloads, and works with technology teams to implement and validate controls. The architect develops secure, resilient, and scalable AI/ML architecture patterns; establishes lifecycle security requirements for AI models, data, and dependencies; and guides teams in risk assessment, safeguards, and security validation. The position conducts threat modeling and risk assessments for AI/ML systems and autonomous agents, covering risks such as model inversion, data poisoning, adversarial attacks, prompt injection, tool abuse, and uncontrolled agent behavior. It also defines trust boundaries, access controls, data protection, and oversight for AI tools and integrations, including Model Context Protocol (MCP). Working with MLOps and Data Science teams, the role embeds automated security controls throughout the machine learning lifecycle, from data ingestion to model deployment. It maintains AI reference architectures and publishes guidance, standards, and patterns for AI technology, development, security, privacy, and observability. The architect advises on AI capabilities and solution design, collaborates across technology, business, governance, cybersecurity, compliance, and privacy teams, and evaluates AI platforms, products, and services against enterprise security requirements. The position also tracks emerging AI threats and vulnerabilities and develops defensive strategies and security observability capabilities.

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 AI/ML security challenges, threat landscapes, and mitigation approaches, including for 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.
Demonstrated ability to assess autonomous-behavior risks and mitigate 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 unfamiliar AI technologies and apply transferable security principles as implementation approaches evolve.
Ability to balance long-term implications with 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 lifecycle security requirements, architecture patterns, and evaluation processes to protect the integrity, confidentiality, and availability of AI models, data, and dependencies.
Guide teams in assessing model risks, implementing safeguards, and validating security before deployment and during operational use.
Conduct AI/ML threat modeling and risk assessments, including risks related to 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, and oversight for AI interactions with enterprise resources, including AI tools, services, integrations, and MCP.
Partner with implementation teams to validate controls protecting contextual data and connected systems.
Collaborate with MLOps and Data Science teams to embed automated, shift-left security controls throughout the machine learning development lifecycle.
Maintain AI reference architectures and publish security guidance, standards, and patterns addressing AI technology, development, security, privacy, and observability.
Consult on AI capabilities across business and technology platforms and align AI architecture frameworks with organizational strategic goals.
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.
Evaluate enterprise AI platforms, products, and services against enterprise security requirements; assess security capabilities, integration risks, and control gaps.
Define safeguards, acceptance criteria, security requirements, and deployment guidance for AI platforms and services, and reassess them as capabilities and business uses change.
Track emerging threats and vulnerabilities targeting AI systems and develop proactive defensive strategies.
Design security capabilities that support AI governance and observability, using threat research to inform their evolution.