clearbank - Senior AI Enablement Engineer
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Requirements
• · Strong background in software engineering, platform engineering, DevEx, or DevOps. • · Hands-on experience using AI-assisted development tools in real engineering environments. • · Experience influencing practices and improving outcomes across multiple delivery teams. • · Ability to evaluate tools and approaches based on evidence and business impact, not hype. • · Strong communication skills, able to explain complex concepts clearly and credibly to a wide engineering audience. • · Outcome-driven and evidence-based in decision making. • · Pragmatic and risk-aware, especially within regulated environments. • · Comfortable operating across ambiguity, rapid change, and emerging technology. • · Collaborative, empathetic, and focused on enabling others to succeed. • · Experience integrating AI into CI/CD pipelines, internal developer platforms, or SDLC tooling. • · Familiarity with engineering productivity and quality metrics. • · Experience working with governance, security, or risk stakeholders. • · Exposure to agentic systems or AI-driven automation within engineering workflows.
Responsibilities
• · Lead the effective and responsible use of AI across ClearBank’s software engineering teams. • · Act as a subject-matter expert on AI-assisted software engineering practices, tooling, and adoption patterns. • · Shape how teams embed AI into the SDLC in ways that improve productivity, quality, and developer experience. • · Drive alignment with stakeholders so AI adoption delivers measurable outcomes rather than anecdotal gains. • · Champion pragmatic governance that enables progress while meeting regulatory and risk expectations. • · Influence engineering practices across teams and help build a coherent AI enablement approach across the bank. • · Master and evaluate AI tooling used in software engineering, including copilots, agentic tools, and workflow-integrated capabilities. • · Collaborate directly with engineering teams to improve how AI is used in coding, testing, review, debugging, and documentation. • · Introduce new AI tools, techniques, or approaches into the engineering community and support adoption at scale. • · Define and measure success using indicators such as DORA and flow metrics, adoption and engagement signals, and code quality or operational outcomes. • · Partner closely with Security, Model Risk, and other stakeholders to keep controls proportionate and non-blocking. • · Occasionally build or extend missing capabilities, including AI-driven services, agents, or platform enhancements that integrate into the SDLC. • · Build relationships with other teams across the bank using AI to share approaches, avoid duplication, and support coherent investment decisions. • · Coach and guide engineers on effective patterns, helping raise capability across the engineering organisation.
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