We're looking for an AI Security Engineer with hands-on expertise in AI/ML, cloud deployments, and security testing of Generative AI and LLM-powered solutions. This role is highly technical, research-driven, and will directly shape how we evaluate, validate, and secure emerging AI-driven cybersecurity tools.
Key Responsibilities
Identify, analyze, and benchmark Generative AI-augmented and LLM-based security solutions in the market.
Conduct proof-of-concept (PoC) assessments of cybersecurity capabilities to validate real-world effectiveness.
Define security control baselines and evaluation criteria for emerging risk-focused solutions.
Evaluate vendor claims, solution architectures, and scalability.
Perform security testing of GenAI-powered cybersecurity tools.
Publish detailed reports on security, compliance, and product efficacy.
Build and integrate AI robustness, vulnerability, and stress testing capabilities within MLOps ecosystems.
Assess and adapt open-source AI security libraries for enterprise-grade stress testing and audits.
Implement secure model development lifecycle practices, including automated white-box and black-box assessments.
Collaborate with application teams to ensure strong developer and customer experiences.
Uphold Blue Box values in all interactions with internal and external teams.
Essential Skills
Must Have:
Strong background in statistics, machine learning (DNN, NLP), big data, and data science toolkits.
Proven experience designing and operationalizing high-performance AI/ML pipelines and production code.
Experience deploying AI/ML models in public cloud environments.
Familiarity with open-source ML tools, frameworks, and libraries.
Proficiency in data science programming languages and toolkits.
Experience with MLOps, DevOps, DataOps, and API integrations.
Hands-on experience with AI workload management.
Strong cloud architecture, design, and operational knowledge.
Good to Have:
Knowledge of adversarial robustness techniques and AI risk management frameworks (e.g., Trustworthy AI).
Familiarity with application security controls (Web, API, Mobile, AI).
Understanding of security frameworks (NIST 800-53, CSF, OWASP ASVS).
Knowledge of Secure SDLC, DevSecOps, and application security design.
Experience with full-stack application architectures (SPAs, REST APIs, SOAP APIs, mobile).
Background in Java, JavaScript, and mobile application development.
Database expertise (Oracle, SQL, DB2, NoSQL).
Cloud security design and operations knowledge.
Exposure to IAM controls (OAuth 2.0, OIDC, JWT).
Strong familiarity with cryptography (data at rest, in motion).
Certifications: CISSP, CISM, CSSLP, CISA, CRISC.
Peer-reviewed publications, conference presentations, or open-source contributions.
Required Skills for Machine Learning with Devops Developer Job