Lead Generative AI Engineer

Overview

In this Lead GenAI Engineer role, you set the technical standard for GenAI work across Motorway’s Data and AI function and own the hardest components yourself. You shape shared foundations—retrieval, evaluation, data pipelines—and ensure production-ready, scalable AI systems. You act as a technical escalation point while coaching senior engineers to raise the team’s impact. You collaborate with product leadership to align architecture with business goals and maintain a defensible view of what good looks like. This is a high-impact opportunity to drive reliable, feature-rich AI in a fast-growing scale-up.

Pay / Benefits

• Competitive salary
• Equity scheme
• 25 days holiday
• Flexible working
• Private medical insurance via BUPA
• Pension scheme

Responsibilities

• Set the architecture standard for complex GenAI work across the function by building reusable, learnable patterns
• Sign-off on materially complex AI systems to preempt architectural, safety, and reliability issues in production
• Own shared foundations determining feature quality: retrieval patterns, evaluation infrastructure, data pipelines, and enabling tooling
• Build the hardest components directly while acting as the escalation point for tough problems
• Design systems for uptime and cost-efficiency with graceful degradation, fallback behavior, and production-scale considerations
• Develop and coach senior engineers, enabling ownership and honest feedback
• Shape hiring and review practices with clear quality criteria
• Provide actionable industry insight to planning, prioritising architecture-driven developments

Key requirements

• Strong Python and SQL in real-world data work
• Deep GenAI stack expertise: retrieval at scale, vector stores, agentic orchestration, multimodal work, evaluation infrastructure, fine-tuning where appropriate
• Fluent with MLOps tooling, deployment practices, and cloud infrastructure (AWS and GCP)
• Proven ability to design production-grade systems and handle incidents when things go wrong
• Ability to explain complex trade-offs to non-technical stakeholders
• Self-awareness to bring in external expertise when needed

• Ability to explain hard technical trade-offs without condescension
• Mentorship and coaching of other engineers
• Strong communication with non-technical stakeholders
• GenAI stack
• Retrieval at scale
• Vector stores

…