Join a well-established technology services and consulting organisation to pioneer and scale AI-driven security governance across the enterprise software development lifecycle (SDLC).
The Role
In this forward-looking engineering role, you'll lead the implementation of governance frameworks and security standards powered by AI and Large Language Models (LLMs), integrating intelligent automation directly into CI/CD pipelines to transform how security policies, compliance checks, and code reviews are enforced across enterprise delivery teams.
Key Responsibilities
- AI-Driven Security Governance - Design, implement, and enforce DevSecOps governance frameworks, security guardrails, and compliance standards utilising AI capabilities and LLMs
- Automated Policy Enforcement - Build and deploy AI-assisted controls for automated code review, policy validation, threat modeling workflows, and security compliance checks within CI/CD pipelines
- Responsible AI & Security Standards - Establish policy guidelines and security baselines for developer AI usage (AI code assistants, generative models, agentic workflows) to manage data privacy, IP, and code vulnerability risks
- Pipeline Integration & Shift-Left - Integrate intelligent security testing and automated remediation guidance into modern CI/CD systems (e.g. GitHub Actions, GitLab CI, Azure DevOps)
- Cross-Functional Leadership - Partner with Security Architecture, Engineering Leads, and Product Teams to define AI governance roadmaps and drive developer adoption across squads
Essential:
- Deep hands-on experience establishing DevSecOps practices, CI/CD security controls, and application security standards in enterprise environments
- Practical experience using AI/LLM tools to automate security governance, policy checking, or code review workflows - not just theoretical understanding
- Proven ability to translate complex regulatory, security, and data privacy requirements into enforceable, automated software controls
- Proficiency with modern CI/CD platforms, scripting (Python, Bash), and APIs to integrate AI workflows into active pipelines
- Excellent stakeholder management skills with a track record of driving security cultural shifts and developer adoption
- Exposure to secure AI deployment frameworks, model risk management, or AI safety standards (e.g. OWASP Top 10 for LLMs, NIST AI RMF)
- Experience with enterprise code scanning tools (SAST, SCA, DAST) and AI-driven triage mechanisms
