Job description

The role involves executing information security, IT risk management processes, and delivering AI/ML security initiatives with a focus on incorporating AI safety controls. Candidates should have experience with secure model development, cloud deployments, and AI/ML operational support.

Responsibility

• Implement secure model development life cycle practices with automated assessments for AI/ML models.
• Deliver and integrate AI robustness, vulnerability, and stress testing capabilities with MLOps ecosystems.
• Evaluate and assess open-source AI security libraries for enterprise AI audit capabilities.
• Implement end-to-end LLM security risk management processes.
• Incorporate machine learning models into diverse application security tooling and processes.
• Support production deployments of AI/ML safety systems using cloud-native packaging and deployment.
• Establish and govern AI/ML and Generative AI application security standards.
• Provide production support and operations for AI/ML security systems.
• Manage cloud deployments and automation frameworks for AI/ML security systems.

Qualifications

• Bachelor's Degree in Data Science, Statistics, Computer Science or Software Engineering.
• 3+ Development Operations Experience.
• 3+ years of software engineering experience.
• Certified Kubernetes Administrator (CKA) and Certified Kubernetes Application Developer (CKAD).
• Master's Degree - Data Science, Statistics, Computer Science, or Software Engineering (Preferred).
• Machine Learning Operation Professional Certifications.
• Strong knowledge of Adversarial Robustness techniques and tools for machine learning.
• Strong knowledge of AI Risk Management frameworks and Trustworthy AI practices.
• Hands-on experience with applying statistics, machine learning algorithms, big data, and data science toolkits.
• Hands-on experience with designing, implementing, and operationalizing AI/ML pipelines.
• Hands-on experience with deploying AI/ML models to cloud environments.
• Hands-on experience with MLOps, DevOps, DataOps and API integrations.
• Hands-on experience with cloud architecture and operations.
• Demonstrated peer-reviewed journal publications, conference presentations, or open-source contributions.