ROLE SUMMARY

We are looking for an AI Security & Privacy Lead to own the end-to-end security posture of our AI/ML systems—spanning Generative AI applications, Agentic AI workflows, RAG pipelines, and production ML infrastructure. You will embed security and privacy by design into every stage of the AI lifecycle, from model training and fine-tuning through deployment, integration, and autonomous agent execution. This role bridges deep AI/ML expertise with offensive security, data privacy engineering, and regulatory compliance to ensure our intelligent systems are trustworthy, resilient, and safe at scale.

KEY RESPONSIBILITIES

• Define and execute the organisation’s AI security strategy covering threat modelling, risk assessment, and security architecture for GenAI, Agentic AI, RAG, and traditional ML systems.
• Lead LLM red-teaming and adversarial testing—design and run prompt injection attacks (direct, indirect, multi-turn), jailbreak assessments, data extraction probes, and model manipulation tests to identify vulnerabilities before production release.
• Architect and implement guardrails, input/output filtering, content moderation, hallucination detection, and toxicity screening pipelines to ensure safe and policy-compliant GenAI outputs.
• Secure Agentic AI systems by enforcing least-privilege tool access, sandboxed execution environments, action approval workflows, human-in-the-loop gates for high-risk operations, and agent behaviour boundary enforcement across multi-agent orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen).
• Design security controls for RAG pipelines—protect vector databases from poisoning attacks, enforce document-level access control in retrieval, prevent sensitive data leakage through embeddings, and validate retrieval-grounded outputs against source authority.
• Own AI data privacy engineering—implement PII detection and redaction, differential privacy, data anonymisation/pseudonymisation, consent management, and data minimisation practices across training datasets, fine-tuning corpora, and inference inputs/outputs.
• Drive compliance with GDPR, CCPA, EU AI Act, NIST AI RMF, ISO 42001, SOC 2, and industry-specific regulations (HIPAA, PCI-DSS) as they apply to AI/ML systems, ensuring audit readiness and documentation.
• Build AI security observability—deploy monitoring for anomalous model behaviour, adversarial input detection, data exfiltration attempts, agent action audit trails, and token-level cost anomaly alerts using SIEM integration and custom telemetry.
• Establish secure MLOps pipelines—model signing, provenance tracking, supply chain security for open-source models (SBOM for AI), secure model registries, encrypted model artefacts, and tamper-proof experiment tracking.
• Develop and deliver AI security training, threat awareness programmes, and secure-by-design guidelines for engineering, data science, and product teams across the organisation.
• Lead incident response for AI-specific security events—prompt injection breaches, model theft, training data poisoning, adversarial attacks in production, and agent autonomy failures.

REQUIRED QUALIFICATIONS

• 8–13 years of combined experience in cybersecurity, AI/ML engineering, or security engineering, with 3+ years focused on AI/ML security.
• Bachelor’s/Master’s in Computer Science, Cybersecurity, AI/ML, or a related field.
• Deep understanding of LLM architectures, transformer internals, fine-tuning workflows, and GenAI application stacks—sufficient to identify and exploit security weaknesses.
• Hands-on experience with LLM red-teaming, prompt injection testing, jailbreak methodologies, and adversarial ML techniques (evasion, poisoning, model inversion, membership inference).
• Strong knowledge of AI privacy techniques: PII detection/redaction (Presidio, spaCy), differential privacy, federated learning, data anonymisation, and privacy-preserving ML.
• Proven experience securing agentic AI systems—tool-use access controls, agent sandboxing, action boundaries, and multi-agent trust frameworks.
• Familiarity with regulatory frameworks: GDPR, CCPA, EU AI Act, NIST AI RMF, ISO 42001, OWASP Top 10 for LLMs, and MITRE ATLAS.
• Proficient in Python, security tooling, and cloud security across AWS, Azure, or GCP.

PREFERRED QUALIFICATIONS

• Experience building AI guardrail frameworks (NVIDIA NeMo Guardrails, Guardrails AI, LLM Guard, Rebuff) and content safety systems.
• Background in offensive security, penetration testing, or red-team operations (OSCP, OSCE, GPEN certifications a plus).
• Hands-on experience with AI governance platforms (Fiddler, Arthur AI, Credo AI, IBM OpenPages) and model explainability tools (SHAP, LIME, Captum).
• Experience with secure multi-tenant RAG architectures, vector DB access controls, and embedding-level data isolation.
• Publications, conference talks, or CTF contributions in AI/ML security; certifications such as CISSP, CCSP, or AI-specific security credentials.

TECHNICAL STACK