Senior Azure Artificial Intelligence Security Architect - Remote

JOB SUMMARY:
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.

ESSENTIAL JOB DUTIES:

Designs Secure AI/ML Architectures: Develops and promotes secure, resilient, and scalable architecture patterns for AI/ML solutions built on Microsoft Azure, Databricks, and other platforms.

AI Model Security: Defines lifecycle security requirements, architecture patterns, and evaluation processes to protect the integrity, confidentiality, and availability of AI models, associated data, and dependencies. Guides teams in assessing model risks, implementing safeguards, and validating security before deployment and during operational use. Updates requirements as model technologies, deployment approaches, and threats evolve.

AI/ML Threat Modeling: Conducts comprehensive threat modeling and risk assessments specifically for AI/ML systems, identifying vulnerabilities related to model inversion, data poisoning, adversarial attacks, and prompt injection.

Agentic AI Security: Leads threat modeling, risk assessment, and security architecture for autonomous AI agents, addressing prompt injection, tool abuse, and uncontrolled agentic behavior. Defines requirements and patterns that constrain agent actions, protect data and connected resources, and establish oversight and accountability. Validates control effectiveness as agent capabilities and operating models evolve.

AI Tools and Integration Security: Defines trust boundaries, access controls, data protection, and oversight for interactions between AI systems and enterprise resources. Establishes supporting security requirements and reusable architecture patterns for AI tools, services, and integrations, including Model Context Protocol (MCP). Partners with implementation teams to validate controls protecting the integrity, confidentiality, and availability of contextual data and connected systems, and updates guidance as integration technologies evolve.

MLSecOps Integration: Collaborates with our MLOps and Data Science teams to embed automated security controls ("shift-left") into the entire machine learning development lifecycle, from data ingestion to model deployment.

Architectural Guidance: Maintains AI reference architectures and publishes security guidance, standards, and patterns addressing AI technology, development, security, privacy, and observability. Updates these architectural resources to guide secure AI implementation across the enterprise.

Strategic Advisory & Business Alignment: Consults on AI capabilities across business and technology platforms, ensuring alignment between AI architecture frameworks and the organization’s strategic goals. Offers actionable recommendations on solution design, risk mitigation, and cross-domain impacts.

Partner & Collaborate: Partners with solution architects, technology leaders, business, governance, cyber security, compliance, and privacy teams to identify AI risks, documents design decisions, and provides enterprise patterns to resolve issues.

AI Platform and Service Security: Evaluates enterprise AI platforms, products, and services against enterprise security requirements, assessing security capabilities, integration risks, and control gaps. Defines safeguards, acceptance criteria, security requirements, and deployment guidance. Partners with technology and governance teams to enable secure adoption and reassesses requirements as platform capabilities and business uses change.

Threat Research and Security Observability: Tracks emerging threats and vulnerabilities targeting AI systems and develops proactive defensive strategies. Designs security capabilities that support AI governance and observability, using threat research to inform their evolution.

Job Qualifications

REQUIRED QUALIFICATIONS:

• At least 5 years of experience in a senior cybersecurity role (e.g., Security Architect, Cloud Security Engineer), or equivalent combination of relevant education and experience.
• Proven, hands-on experience securing AI/ML workloads and data pipelines within Microsoft Azure and Databricks platforms.
• Demonstrated 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, with the ability to identify 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.
• Demonstrated 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 with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.
• Displays intellectual curiosity, integrity, and is motivated by achieving long-term business outcomes.
• Ability to work effectively in a team environment and lead cross-functional teams.
  
  

PREFERRED QUALIFICATIONS:

• Practical experience applying agentic AI security architecture, implementation requirements, and control validation approaches to autonomous systems. Demonstrated ability to assess autonomous-behavior risks and apply mitigations for common attack vectors.
• Experience with Model Context Protocol (MCP) and securing the infrastructure that supports it.
• Familiarity with applying MLSecOps principles through security-testing and automation tools in Continuous Integration and Continuous Delivery/Continuous Deployment (CI/CD) pipelines.
• Professional certifications such as Certified Information Systems Security Professional (CISSP), Certified Cloud Security Professional (CCSP), or Azure Security Engineer Associate/Expert.
• Experience translating governance, compliance, security, and privacy management requirements into security architecture.
• Familiarity with requirements under the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health (HITECH) Act, and ability to partner with privacy, legal, and compliance teams to define AI data-handling controls, document design decisions, and produce validation evidence.
• Experience supporting agile teams by providing guidance on design, opportunities, impact, and risks, taking account of 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 the long-term (big picture) and short-term implications of individual decisions.
  
  

To all current Molina employees: If you are interested in applying for this position, please apply through the Internal Job Board.

Molina Healthcare offers a competitive benefits and compensation package. Molina Healthcare is an Equal Opportunity Employer (EOE) M/F/D/V.