Lead AI Security Architect
Summary
Lead the design of secure AI platforms, embedding privacy, compliance, and risk controls into ML pipelines and model deployments while aligning with regulations like PDPA.
As a Lead AI Security & Governance Architect., you will embed security, privacy, and compliance into AI platforms, ensuring secure-by-design deployments that balance innovation with regulatory obligations.
Key Responsibilities
- Define and implement secure AI/ML architectural principles across data pipelines, model deployment, and access layers.
- Drive Responsible AI (RAI) and Explainable AI (XAI) practices for fairness, transparency, and trust.
- Ensure compliance with PDPA, enterprise policies, and emerging AI governance standards.
- Conduct AI-specific risk assessments (bias, adversarial attacks, data leakage, LLM vulnerabilities).
- Integrate privacy-preserving mechanisms (encryption, anonymization, tokenization).
- Evaluate and recommend AI security/governance tools (AWS Guardrails, Azure Responsible AI, IBM Watson Governance).
- Collaborate with internal teams, vendors, and partners to ensure secure, compliant AI solutions.
- Champion AI security awareness and best practices across the organization.
Key Requirements
- Bachelor's/Master's in Cybersecurity, Engineering, AI/ML, or related field.
- 5+ years in cybersecurity/data governance/secure systems architecture, with 3+ years in AI or cloud ML.
- Strong knowledge of AI/ML risks (model misuse, prompt injection, bias, adversarial inputs).
- Hands-on experience with cloud AI platforms (AWS SageMaker, Azure ML, Google Vertex AI).
- Familiarity with AIOps/LLMOps, DevSecOps, RBAC, secrets management, secure CI/CD.
- Knowledge of data privacy controls and frameworks (Zero Trust, OWASP for ML).
- Excellent communication, stakeholder management, and technical writing skills.
- Telco industry experience is a plus.
