AI Enablement Lead for Scalable SDLC & DevOps

Jobtailor

Greater London

On-site

GBP 90,000 - 130,000

Full time

14 days+

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Job summary

ClearBank is hiring to lead the responsible use of AI across its software engineering teams in London. You will be the AI subject‑matter expert, shaping how teams embed AI into the SDLC to boost productivity, quality, and developer experience.

You will collaborate with Security and Risk to ensure proportionate controls, evaluate AI tooling, and coach engineers on effective patterns. Hands‑on AI‑assisted development experience and a track record of delivering measurable outcomes are required.

Qualifications

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.