Job Description
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.