Senior AI Enablement Engineer

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.

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.

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