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.