Senior AI Security Engineer

Assess the security posture of LLMs and generative AI systems used within or by Applied Systems Run threat modeling and security architecture reviews for AI/ML systems and their data pipelines Implement and maintain security controls across model training, fine-tuning, and inference infrastructure Identify and help remediate AI-specific vulnerabilities — prompt injection, model poisoning, data exfiltration, and adversarial attacks Build data security and privacy controls for training datasets and inference inputs Develop security baselines, hardening configs, and incident-response runbooks for AI platforms Implement monitoring and detection for anomalous AI system behavior Contribute to internal AI security policies, vendor reviews, and security training

3–5+ years in security engineering or DevSecOps Foundational understanding of ML concepts, workflows, and frameworks (PyTorch, TensorFlow, scikit-learn) Working knowledge of LLMs, transformer architectures, and their security concerns (prompt injection, jailbreaking, data poisoning, model extraction) Container and Kubernetes security experience, including securing AI workloads Hands-on Python plus Bash or Go, and comfort with common data science libraries Cloud security in AI contexts — GPU security, distributed training, and data protection in ML pipelines Infrastructure-as-code experience (Terraform, Ansible) Solid grounding in encryption, key management, and PII/data privacy Familiarity with secure SDLC, ML supply-chain security, and model governance/versioning Working knowledge of compliance frameworks as applied to AI (GDPR, SOC 2) Strong communication skills and the ability to work independently or on a team

Security certification (Security+, CISSP) Hands-on experience with OpenAI / Anthropic Claude / Vertex AI / Azure OpenAI ML security tools (Robust Intelligence, Arthur AI) AI security research or publications Penetration testing Red-teaming AI systems