Cloud Artificial Intelligence Security Lead
On-site · Tampa, Florida, United States
Job Summary
Cloud Artificial Intelligence Security Lead responsible for defining and implementing security design patterns for cloud-based AI services and ensuring an AI cloud security framework supports the life cycle of AF’s AI-powered solutions. Collaborates with data governance, compliance SMEs, data scientists, GenAI specialists, developers, and MLOps engineers to identify security vulnerabilities, implement best practices, and ensure regulatory compliance (NIST, SOC 2, and others). Leads security assessments, penetration testing coordination, threat modeling, and vulnerability management; supports secure AI/ML development, safeguarding PII; stays ahead of evolving cybersecurity threats and privacy regulations; documents security protocols, conducts training, and promotes security awareness; engages stakeholders to tailor security policies for AI/ML products; designs and executes GenAI security test approaches, collaborates with QA on test plans to verify AI system compliance, and contributes to incident response and security architecture reviews; focuses on secure configuration of AI tools and ethical considerations in AI systems.
Required Qualifications
- Bachelor's degree in computer science, electrical or computer engineering, statistics, econometrics, or related field, or equivalent work experience
- 10+ years of hands-on experience in cybersecurity or information security
- 4+ years of experience with Natural Language Processing (NLP) and Large Language Models (LLM) desired
- 4+ years of experience working in Microsoft Azure cloud environments (e.g. Azure Cloud Services, Azure Fabric, Azure Data Factory, Purview Data Governance), as well as Azure AI services, as well as data cataloging practices
- Familiarity with AI testing frameworks and tools such as TensorFlow, PyTorch, or Kerns
- Deep understanding of Machine Learning lifecycles and MLOps
- Deep understanding of the security challenges and controls for Large Language Models (LLMs), including prompt injections, data poisoning, and model theft
- Demonstrated proficiency with AI/ML fundamental concepts and technologies including ML, deep learning, NLP, and computer vision
- Experience assessing AI systems for ethical considerations and potential biases to make sure they follow ethical standards and encourage inclusivity and diversity
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