Senior DevSecOps Engineer

About the role

Location: Remote Salary: £75,000 - £85,000 About us At Arbor, we're on a mission to transform the way schools work for the better. We believe in a future of work in schools where being challenged doesn't mean being burnt out and overworked. Where data guides progress without overwhelming staff. And where everyone working in a school is reminded why they got into education every day. Our MIS and school management tools are already making a difference in over 12,000 schools and trusts. Giving time and power back to staff, turning data into clear, actionable insights, and supporting happier working days. At the heart of our brand is a recognition that the challenges schools face today aren't just about efficiency, outputs and productivity - but about creating happier working lives for the people who drive education everyday: the staff. We want to make schools more joyful places to work, as well as learn. About the role We are looking for an experienced and diligent Senior DevSecOps Engineer to join our DevSecOps team and help us secure the resilience, integrity, and performance of the Arbor platform as it scales - including the AI-enabled systems and developer tooling now central to how we build and operate. The remit and focus of the role is to combine deep security engineering with a secure-by-design mindset, using metrics, automation, and threat modelling to drive measurable improvements. Working closely with architecture, platform, and engineering teams, you will continuously harden our infrastructure, our software supply chain, and the AI systems and agents increasingly embedded across our products and workflows. It's a broad and exciting role, so we're looking for someone up for a challenge - if you're a collaborative and security conscious candidate, this is the role for you. Core responsibilities Collaborate with stakeholders to pinpoint security enhancements across platform architecture and infrastructure, devising and executing strategic plans for implementation Work closely with the Platform team to embed robust security processes, controls, and tooling across all system components Threat model new and existing systems - including AI/LLM-enabled features and agentic workflows - and translate findings into prioritised, actionable work Strengthen our software supply chain: dependency and base-image hygiene, SBOM generation, artefact signing and provenance, and the pinning of third-party actions and packages Secure the use of AI across the SDLC, ensuring agentic coding tools, assistants, and MCP integrations operate within safe, well-scoped, and auditable boundaries Contribute to the evolution of deployment frameworks, emphasising security, deployment speed, and system stability Elevate platform security through strong secrets management and the safe handling of sensitive information Play an active role in incident response, resolution, and blameless post-mortems, facilitating continuous improvement Participate in knowledge-sharing initiatives, including tech-talks and team-based learning sessions Maintain meticulous, current documentation - playbooks, runbooks, and comprehensive systems documentation - to facilitate knowledge dissemination About you Extensive experience in cyber security and associated engineering practices Vulnerability management and remediation at scale Proven track record in DevOps / DevSecOps engineering within large-scale platforms Proficiency in distributed cloud systems, particularly Amazon Web Services Expertise in Infrastructure as Code (IaC) tooling such as Terraform and CloudFormation Experience with languages such as PHP, Bash, or Python Experience with Docker and containerisation, with a working understanding of container and runtime security Software supply-chain security: SBOMs, dependency scanning, and artefact signing / provenance (e.g. SLSA, Sigstore) Secrets management and detection (e.g. Vault, cloud-native secret stores, secret-scanning in CI) Security tooling across the SDLC: SAST, DAST, SCA, IaC scanning, and container scanning (e.g. Snyk, Trivy) Policy-as-code and guardrails (e.g. OPA / Conftest), with an identity-centric / zero-trust approach to access Familiarity with monitoring and detection tooling like DataDog, Prometheus, or similar platforms A proactive problem-solving attitude coupled with strong teamwork and communication skills Exceptional proficiency in written and spoken English to effectively articulate ideas and concepts AI security and safe AI usage Practical understanding of AI/LLM security risks and their mitigations - e.g. prompt injection, jailbreaks, insecure output handling, sensitive-data leakage, and excessive agency (aligned to the OWASP Top 10 for LLM Applications) Experience securing AI-assisted and agentic development tooling: scoping permissions, sandboxing, logging and audit, and preventing secret or data exfiltration through AI agents and MCP servers Familiarity with AI threat modelling and adversarial techniques (e.g. MITRE ATLAS

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