Software Engineer (AI Enablement)

Role: Software Engineer (AI Enablement)
Location: London (Hybrid working, 2 days per week in office - Wed/Thurs)
Reporting to: Engineering Manager – AI Enablement

The Role

Our AI Enablement team is dedicated to enhancing the efficiency with which product and engineering teams deliver value through the safe and effective adoption of AI-native tools, practices, and workflows. We collaborate across the organisation to minimise friction, boost developer productivity, and facilitate the governed integration of AI at scale.

In our role as enablers, we work closely with product and engineering teams to ensure the safe and effective adoption of AI practices and tools by providing internal platforms, agents, and workflows that eliminate friction throughout the product development lifecycle.

As builders, we engage in experimentation and deployment of AI-native capabilities that demonstrate the possibilities, establish effective patterns, and facilitate scalability. Our success is measured by tangible adoption and quantifiable acceleration in delivery.

What you’ll do

Build & operate production‑grade AI tools, agents, and workflows

• Develop, enhance, and integrate tools that facilitate the entire product development lifecycle: ideation/discovery, specification review & early feedback, code generation/refactoring/review, documentation generation, test generation & quality validation, observability/log analysis/incident support. Take ownership of reliability, performance, cost, and telemetry.
• Establish evaluated workflows with transparent metrics (usage, velocity, model performance), enabling teams to trust and adopt them effectively.
• Deliver comprehensive AI workflows, for example:
• Agents that analyse logs and metrics to identify bugs, risks, performance, or efficiency issues.
• Automated pull requests that propose fixes or improvements.
• AI-assisted review of design documents and Architecture Decision Records (ADRs), incorporating observability, safeguards, and rollback capabilities from the outset.

Partner,synthesise & scale AI workflows, agents and best practices:

• Adopt agents and identify workflows, patterns, tools, and best practices developed in silos or for specific use cases, transforming them into department-wide capabilities.
• Establish frameworks, guardrails, and processes that foster transparency and facilitate the safe, low-friction adoption of agents across teams.
• Collaborate closely with teams to integrate agents or tools into daily practices. Document processes, conduct hands-on pilots, gather feedback, and scale successful patterns across the department. Drive adoption.

Standards, Safety & Governance:

• Define and maintain guardrails for AI usage within our environment; manage risks related to security, data privacy, and supplier constraints; and translate governance into actionable engineering patterns.

Measure Impact:

• Oversee the comprehensive lifecycle from problem identification to solution design, implementation, deployment, and impact assessment, ensuring that trade-offs are transparent and informed by data.

What you’ll bring:

• Robust engineering fundamentals with experience in building and managing production systems.
• A deep passion and understanding of the current AI landscape and its transformative impact on product development and software engineering.
• A product-centric mindset, measuring success through adoption and outcomes. Proficient in rapid experimentation and adept at transforming ambiguous challenges into practical, scalable solutions.
• Hands-on experience with LLM APIs and AI engineering patterns (prompting, MCP integrations, tool-calling/functions, RAG, evaluations, agent orchestration, agentic platforms) and an understanding of the operational realities in production (observability, security, cost controls, data privacy).
• Familiarity with evaluation frameworks and success metrics for AI within engineering workflows.

Already AI‑native:

You consistently leverage AI to enhance ideation, discovery, specification generation, coding, debugging, reviews, documentation, and planning. You view AI as a collaborator, thinking in terms of agents, workflows, and automation, while iterating swiftly: experiment → learn → refine → scale, keeping risk and outcomes at the forefront of your efforts.

Preferred experience:

• We’re looking for someone with demonstrated experience in building and orchestrating AI agents or multi-step workflows within production environments for technology-driven product organizations. Your skills in enhancing operational efficiency will be a great asset!
• A proven background in integrating AI into the product development lifecycle is essential. We value your commitment to innovation and continuous improvement!
• Familiarity with evaluation frameworks and success metrics for assessing AI impact in engineering workflows is a plus! Your ability to contribute to data-driven decision-making will make a difference.