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Z
Zelis
AI Security Architect
Career Insights for Artificial Intelligence Engineer (General)
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What they do
An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
$129,894 / year median in New Jersey
Job Description
At Zelis, we Get Stuff Done. So, let's get to it! A Little About Us Zelis is modernizing the healthcare financial experience across payers, providers, and healthcare consumers. We serve more than 750 payers, including the top five national health plans, regional health plans, TPAs and millions of healthcare providers and consumers across our platform of solutions. Zelis sees across the system to identify, optimize, and solve problems holistically with technology built by healthcare experts - driving real, measurable results for clients. A Little About You You bring a unique blend of personality and professional expertise to your work, inspiring others with your passion and dedication. Your career is a testament to your diverse experiences, community involvement, and the valuable lessons you've learned along the way. You are more than just your resume; you are a reflection of your achievements, the knowledge you've gained, and the personal interests that shape who you are. Position Overview Enterprise-level expert responsible for defining and guiding the strategic direction of security architecture and risk posture. AI Security Architect - Job Description Overview The AI Security Architect is responsible for designing, implementing, and governing security frameworks and requirements to enable the secure use of AI and related infrastructure. This role ensures AI technologies are developed and deployed securely, ethically, and in compliance with regulatory and organizational requirements. This role will be part of cybersecurity organization with a matrix line to the AI team. Key Responsibilities Develop secure-by-design architecture guidelines for AI/ML platforms, including data ingestion, model training, model deployment, and inference layers. Define reference security architectures, patterns, and guardrails that enable secure AI development while minimizing manual review and approval friction. Partner with respective engineering teams to automate / bake in the security guardrails where possible. Design risk-based, proportionate AI security controls that satisfy regulatory and enterprise requirements while enabling rapid AI development and experimentation. Identify threats unique to AI systems-model inversion, poisoning, evasion attacks, data leakage, prompt injection, etc. Evaluate emerging AI security threats, tools, and best practices. Lead AI-specific risk assessments and security design reviews. Work with red teams to validate model robustness against adversarial attacks. Establish security policies for ethical AI use. Ensure compliance with enterprise, industry, and regulatory frameworks (e.g., NIST