Description

You will secure the machine learning lifecycle by evaluating emerging Generative AI and LLM security solutions.

Responsibilities

• Benchmark and conduct proof-of-concept assessments of Generative AI and LLM agentic security solutions.
• Define security control baselines, evaluation criteria, and technical scalability requirements for emerging risk solutions.
• Integrate AI robustness, vulnerability, and stress testing capabilities into MLOps ecosystems.
• Implement secure model development life cycle practices using automated white box and black box assessments.
• Evaluate open-source AI security libraries to build enterprise-grade stress testing and audit capabilities.

Required Skills

• Bachelor’s Degree in Data Science, Statistics, Computer Science, or Software Engineering.
• 3+ years of software engineering experience.
• 2+ years of experience with Machine Learning Application Development.
• Hands-on experience designing, implementing, and operationalizing high-performance AI/ML pipelines.
• Experience deploying and operationalizing AI/ML models to public cloud environments.
• Proficiency with MLOps, DevOps, DataOps, and API integrations.
• Knowledge of application security controls including Web, API, and Mobile.
• Familiarity with security frameworks such as NIST 800-53, CSF, and OWASP ASVS.
• Technical proficiency in Java, Javascript, SQL, Oracle, DB2, and NoSQL databases.

Preferred Skills

• Master’s Degree in a quantitative field and MLOps Professional Certifications.
• Deep knowledge of Adversarial Robustness techniques and AI Risk Management frameworks.
• Experience with IAM controls (OAuth 2.0, OIDC, JWT) and cryptography controls.

Key Skills

Education

Bachelor's degree