AI Security Engineer (Agentic Systems)

About Us: ClearRoute is an engineering consultancy bridging Quality Engineering, Cloud Platforms and Developer Experience. We help enterprises reliably bring high-impact digital products to market faster, cheaper, and safer, working with technology leaders facing complex business challenges. We take as much pride in our people, culture and work-life balance as we do in making better software. We’re not just making better software. We’re making the making of software better. Collaborative, entrepreneurial and dedicated to problem solving, we bring the step change our customer need to sustain innovation. Our values challenge us to do the best we can for ClearRoute, our customers and most importantly our team. This is an opportunity for you to build the organisation from the ground up, use your voice to drive change and help transform organisations and problem domains. Role This role sits apart from our traditional cloud security engineering positions: it is centred on AI and agentic systems rather than infrastructure alone. You will test and secure AI use cases across an account, not a single application or user set, building a broad, first-hand picture of how ClearRoute should approach testing, risk and governance for agentic AI. You will work across teams rather than within one delivery stream, owning the question of what "good" looks like for agentic security testing across the account, and helping ClearRoute build a repeatable, defensible approach that other engagements can adopt. The role is deliberately hands-on in its first phase: you will implement and run the testing yourself, so you understand the detail before asking anyone else to follow it. As that practice matures, the role shifts toward governance across the account, training and upskilling existing security engineers to implement and automate the strategy themselves, so agentic security testing becomes something the wider team can run, not something that depends on one person. Key Responsibilities Agentic Red Teaming & Adversarial Testing: Design and run red-team exercises against AI agents and agentic workflows, probing for prompt injection (direct and indirect), tool-call and argument manipulation, agent hijacking, memory/context poisoning, and privilege or credential escalation. Build repeatable evaluation approaches for non-deterministic systems, including how to test, measure and report against outputs that vary run to run. Define and own an approach for detecting, measuring and mitigating hallucinations in agent and LLM outputs, distinct from traditional functional-testing methods. Agentic Governance & Risk: Own agentic AI risk identification and governance across an account, not a single application: build the risk view, the test strategy and the tooling recommendations that other teams can reuse. Map findings and controls to recognised external frameworks (for example OWASP's agent-specific risk taxonomy, NIST's AI Risk Management…