AI Cybersecurity Engineer

About the role

We are seeking an AI Cybersecurity Engineer to serve as a technical security lead and architect interfacing with our company’s various AI initiatives.

Responsibilities

• Design and architect enterprise-grade, secure AI security platforms that protect ML models, training pipelines, inference systems, and AI-driven applications from sophisticated adversarial attacks.
• Define and drive the technical vision and security roadmap for all AI/ML initiatives across the organization, embedding security into the complete AI lifecycle from development through deployment and monitoring.
• Lead architectural reviews and provide authoritative technical guidance on security architecture patterns, threat models, and risk mitigation strategies for AI systems.
• Establish security standards and frameworks for AI development, incorporating OWASP LLM Top 10, MITRE ATLAS, NIST AI Risk Management Framework, and other industry best practices.
• Develop security controls for AI model training, validation, deployment, and monitoring including input/output filtering, model integrity validation, and behavioral anomaly detection.
• Implement data security and privacy controls across AI workflows including sensitive data detection, data loss prevention for AI prompts and responses, and confidential computing techniques.
• Build automated security testing frameworks for continuous validation of AI model security posture and detection of adversarial attack patterns.
• Engineer AI-powered security detection systems leveraging machine learning for threat hunting, anomaly detection, and behavioral analytics.
• Communicate complex technical concepts to non-technical executives and business leaders, translating security risks into business impact and strategic recommendations.
• Serve as the technical authority and trusted advisor on AI security matters for senior leadership including CISO and CTO.
• Develop and enforce AI security governance policies, standards, and guidelines that ensure ethical, safe, and compliant use of AI across the enterprise.
• Establish AI model governance frameworks addressing model validation, bias detection, explainability requirements, and audit trails.
• Implement continuous monitoring and observability for AI systems to detect model drift, performance degradation, and security anomalies in real-time.

Requirements

• Bachelor's degree in Computer Science, Cybersecurity, Information Security, Software Engineering, or related technical field preferred.
• Advanced coursework or specialization in artificial intelligence, machine learning, cryptography, or secure systems design.
• A minimum of 10 years of progressive experience in cybersecurity engineering, with at least 2+ years focused on AI/ML security, application security, or security architecture.
• Deep expertise in AI/ML security principles including adversarial machine learning, model security, data poisoning detection, and prompt injection defense.
• Expert-level knowledge of AI/ML frameworks and platforms (TensorFlow, PyTorch, scikit-learn, Hugging Face) and their security implications.
• Extensive experience with cloud security architectures on AWS, Azure, OCI, or GCP, specifically securing AI/ML workloads in cloud environments.
• Strong proficiency in programming languages including Python (primary), Java, C#, Go, or similar with emphasis on secure coding practices.
• Proven experience designing and implementing security for LLMs and generative AI systems including RAG architectures, vector databases, and agent frameworks.
• Hands-on expertise with MLOps/MLSecOps toolchains, CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure-as-code.
• Deep understanding of security frameworks and standards: OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO 27001, SOC 2.
• Strong knowledge of cryptography, authentication/authorization protocols, zero-trust architectures, and identity security principles.
• Demonstrated ability to securely integrate AI/ML solutions with existing legacy applications (e.g., ERP, CRM, mainframe, or on-prem systems) using modern integration patterns (APIs, gateways, middleware, or RPA), while enforcing enterprise security controls such as RBAC, encryption, logging, and compliance with data governance standards.
• Exceptional communication skills with ability to articulate complex security and AI concepts to both technical and executive audiences.
• Strategic thinking with ability to balance immediate security needs with long-term architectural vision.
• Strong problem-solving capabilities and critical thinking when examining novel threat patterns and security challenges.
• Collaborative mindset with proven ability to influence without authority and build consensus across diverse stakeholders.
• Adaptability and continuous learning orientation given the rapidly evolving AI security landscape.

Qualifications

• Preferred qualifications include:
• CASP (Certified AI Security Professional) or equivalent AI security certification.
• CISSP (Certified Information Systems Security Professional), CISM (Certified Information Security Manager), or CCSP (Certified Cloud Security Professional).
• Cloud security certifications: AWS Security Specialty, Azure Security Engineer, or GCP Professional Cloud Security Engineer.
• AI/ML certifications from recognized providers (Google, AWS, Microsoft, DeepLearning.AI).
• Familiarity with AI governance frameworks and responsible AI principles.

Benefits

SEI offers a wide range of benefits including comprehensive care for your physical and mental well-being, a strong retirement plan, tuition reimbursement, a hybrid working environment for most roles, support for working parents and flexible Paid Time Off (PTO) so you can relax, recharge and be there for the people you care about.