Securing enterprise AI solutions across the entire AI lifecycle, from data ingestion and model training to deployment and inference, by mitigating risks and developing innovative security controls.
Requirements
- 10+ years of progressive experience in cybersecurity, with experience directly focused on securing AI/ML systems and MLOps pipelines in an enterprise environment.
- Extensive experience with cloud security principles and practices (AWS, Azure, GCP).
- Hands-on experience with a wide range of security tools and technologies, including but not limited to SIEM, SOAR, EDR, network security, application security, and specifically AI/ML security toolkits.
- Demonstrable experience with threat modeling frameworks (e.g., STRIDE, MITRE ATT&CK for ML) and conducting comprehensive risk assessments for AI.
- Strong programming skills in Python, with experience in developing secure code and security automation.
- Expertise in secure software development lifecycles (SSDLC) and DevSecOps principles.
- Experience with containerization technologies (Docker, Kubernetes) and securing containerized environments.
Responsibilities
- Develop and champion an AI security roadmap aligning with business objectives.
- Act as the subject matter expert and technical lead for AI security initiatives, guiding cross-functional teams (ML engineers, data scientists, software engineers, DevOps, and traditional security teams).
- Conduct in-depth threat modeling and risk assessments specifically tailored to AI/ML systems (e.g., adversarial attacks, data poisoning, model inversion, prompt injection, supply chain attacks on ML pipelines).
- Design, implement, and integrate security controls into AI/ML pipelines, MLOps platforms, and AI-powered applications.
- Evaluate, select, and deploy commercial and open-source security tools and technologies relevant to AI.
- Develop custom security solutions and frameworks where off-the-shelf options are insufficient to address unique AI security challenges.
- Implement and manage security measures for AI data (e.g., secure data storage, access controls, encryption, data lineage, and anomaly detection for data drift).
Other
- Bachelor's degree in Computer Science, Cybersecurity, Artificial Intelligence, Machine Learning, or a related highly technical field. Master's or Ph.D. preferred.
- Ability to communicate complex technical concepts effectively to both technical and non-technical stakeholders.
- Strong leadership capabilities with the ability to influence and drive change.
- Self-motivated, proactive, and capable of working independently as well as collaboratively in a fast-paced environment.
- Mentor and educate engineering teams on secure AI development practices.
