Senior AI Security and Governance Engineer

Responsibilities

• Lead AI‑specific threat modeling across the full AI lifecycle covering prompt injection, data leakage, model poisoning, adversarial attacks, tool abuse, privilege escalation, and agentic supply‑chain risks.
• Define and enforce secure AI architecture patterns and prohibited design anti‑patterns for LLM‑powered applications, autonomous agents, and multi‑agent workflows.
• Partner with product and platform engineering teams to embed security controls natively into AI development pipelines (S‑SDLC / Secure AI Development Lifecycle), including secure CI/CD gates, pre‑production reviews, and post‑deployment monitoring.
• Design and ope rationalise runtime protections for AI systems including prompt injection detection, jailbreak protection, output content controls, and abuse detection for high‑throughput environments.
• Define Human‑on‑the‑Loop (HOTL) review checkpoints for autonomous agentic workflows where high‑risk decisions require human oversight before execution.
• Establish AI‑specific data classification policies and enforce data boundary controls, retention limits, and usage constraints for data ingested by or generated by AI systems.

• Minimum of a Bachelor’s (Preferred Master’s); preferably Computer Science/Computer Engineering or a related field
• 8–12+ years in security architecture, application security, cloud security, or a closely related field.
• 3+ years of hands‑on experience securing AI/ML or LLM‑based systems in enterprise environments, including practical knowledge of prompt injection, data exfiltration through AI APIs, and agentic risk.
• Demonstrated experience defining and implementing AI governance frameworks (OWASP Top 10 for LLM, NIST AI RMF, ISO/IEC 42001, EU AI Act, or equivalent).
• Strong background in threat modelling, secure design review, and risk management across complex distributed systems.
• Hands‑on experience with data loss prevention (DLP), CASB, SWG, or equivalent technologies applied to AI and SaaS environments.
• Experience designing and enforcing granular access control frameworks (RBAC, ABAC) for AI agents, tools, and data pipelines.
• Strong written and verbal communication skills including executive‑level reporting and the ability to translate complex AI risk into business language.
• Ability to read and review code (Python, JavaScript/Type Script, or similar) to understand AI workflows, APIs, and failure modes.

Expertise in AI security architecture and threat modelling, with a focus on securing AI/ML systems and implementing governance frameworks. Proficient in designing secure AI development pipelines and ope rationalising runtime protections for high‑risk AI environments.