Lead multidisciplinary engineering teams across AI/ML, data, software and platform to ship AI capability that gets trusted and adopted
Turn loosely defined ideas into delivery plans, milestones and measurable outcomes, from discovery through build and launch to iteration
Split your time between running the team and doing the work alongside them, reviewing designs and code and prototyping where that’s quicker than explaining
Own the technical calls on deployment, routing, evaluation and monitoring across LLM and ML systems, including multi-provider setups, fallback strategy and cost and latency trade-offs
Act as technical backstop for agent and tool-use work, including orchestration patterns and the judgement to know when an agentic approach is unnecessary
Take technical ownership of a natural-language-to-SQL platform and the integrations sitting around the internal AI tooling
Decide when a problem genuinely needs fine-tuning versus better prompting, retrieval or routing, and back the call with evaluation data
Keep what ships secure, observable, cost-sensible and reliable, and represent AI delivery credibly to senior stakeholders
Manage the people side properly, with coaching, growth and honest feedback
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