Principal AI Engineer

• Confidence operating with senior clients and executives while remaining credible with engineers at code and architecture level
• Deep technical credibility. You’re comfortable working in production Python, substantial codebases and API-driven systems that need to perform reliably at scale
• Experience leading complex programmes or multiple concurrent engineering workstreams, with accountability for technical direction, planning, resourcing, risk and delivery outcomes
• Recent, personal experience architecting and building production AI systems. Expect to discuss retrieval strategy, caching, context economics, serving constraints, evaluation, observability and what went wrong in practice
• The judgement to know when to intervene personally and when to lead through others, delegating effectively without giving up accountability for technical quality
• Experience contributing to account growth, technical propositions or go-to-market activity within a consulting organisation
• At least 10 years’ experience across AI, data and software engineering, including 3+ years leading engineering teams or a substantial technical function within consulting or professional services
• Experience operating beyond individual project leadership, with responsibility for technical direction, engineering quality or capability across multiple teams
• Experience developing senior engineers and technical leaders, shaping team capability and raising the engineering bar across a wider organisation
• Commercial awareness sufficient to turn a technical solution into a realistic scope, team shape, estimate and delivery plan, and to challenge assumptions that do not hold up
• Strong systems thinking. You can reason through unfamiliar platforms and problems rather than relying on expertise in a single stack
• Strong experience with Databricks and Azure OpenAI, which underpin much of our delivery
• The ability to make difficult technical calls, create clarity where there is ambiguity and take responsibility for the outcome
• Practical experience designing systems around EU AI Act requirements
• A strong presence in the Databricks or Microsoft partner ecosystem
• Ontology, knowledge graph or semantic layer experience
• Delivery experience in pharma or CPG
• Multilingual AI systems in production
• Experience shaping go-to-market and commercial strategy for an AI Engineering practice

Key responsibilities

• Set technical direction across AI Engineering, defining architecture principles, engineering standards, and delivery practices.
• Own the technical quality of major AI engagements, particularly where architecture, scale, complexity, or delivery risk requires senior leadership.
• Develop senior engineers and technical leads, building the leadership depth and succession required to scale the department.