Senior AI Enablement Engineer

Responsibilities

• Lead the effective and responsible use of AI across Clear Bank’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.

• Strong background in software engineering, platform engineering, Dev Ex, or Dev Ops.
• 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.