AI Platform Engineer - Security & Governance
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Job description
roads for others, and raising the engineering quality bar for AI development across the organization. About the role We are looking for a Senior AI Platform Engineer - Security & Governance to join the AI Platform Team and help define and implement guardrails, privacy patterns, governance mechanisms, and safe defaults that allow AI adoption to scale responsibly across Awin. In this role, you will work on the safety and control layer of AI development: AI security patterns, privacy and PII handling guidance, provider and model governance, reusable guardrails, and lightweight readiness mechanisms that improve safety without creating unnecessary process. This role is best suited to someone who combines strong engineering judgment with real practical experience in security, governance, privacy, or compliance for AI-powered systems. We care more about evidence of sound decision-making and pragmatic implementation than about titles or years in a specific niche. What you'll do Define and
Requirements
improve practical guardrails and safe defaults for AI-powered systems Help shape Awin's approach to privacy, PII handling, and data-safe patterns for AI workflows Contribute to approved provider and model guidance, including practical validation and usage expectations Work with engineers to translate policy, risk, and governance needs into reusable platform patterns and engineering controls Help define production-ready expectations for AI systems from a safety, governance, and risk perspective Contribute to readiness checks, automated validations, and scalable governance mechanisms that reduce reliance on manual approval processes Help improve logging, tracing, and observability practices so AI systems are auditable and safer to operate Partner closely with Security, Legal, Architecture, and product engineering teams to ensure standards are practical and proportionate Support teams in understanding AI-specific risks such as unsafe outputs, provider misuse, privacy exposure, and poor trace hygiene Write clear documentation, patterns, and guidance that help teams apply standards consistently Contribute to strong engineering practices across the team through clean code, testing, and collaboration Mentor others and help raise the team's overall maturity in AI safety, governance, and privacy-aware engineering Requirements Strong software engineering fundamentals and experience working on production systems Practical experience in security, governance, privacy, compliance, or risk-related engineering work Clear hands-on experience applying those controls to AI- or LLM-powered systems Strong understanding of AI-specific risks, including privacy exposure, PII handling, unsafe outputs, provider/model risk, and misuse or abuse patterns Experience translating policy or risk requirements into practical engineering controls, patterns, or defaults Good understanding of how to make AI systems observable, auditable, and safer to operate in production Ability to work effectively with engineers, architects, and non-engineering stakeholders such as Security or Legal Comfortable working across the lifecycle of a platform capability, from design and implementation to rollout and iteration Good understanding of information security and how to design solutions with security in mind Comfortable applying unit testing, continuous integration, and continuous deployment. Strong communication skills, both synchronous and asynchronous Comfortable working through ambiguity and helping shape standards where the right balance between safety and speed is still evolving Strong judgment and pragmatism, with an ability to improve safety without introducing excessive bureaucracy Nice to have Experience designing or implementing guardrails for AI or LLM-powered systems Experience with AI governance automation, readiness gates, policy checks, or preflight validation patterns Familiarity with tracing, auditability, and logging standards for AI systems Familiarity with evaluation workflows, regression checks, and production readiness practices for AI Experience assessing