AI Engineering Lead

Role overview Lead a small, distributed engineering team that ships production-grade agentic AI systems for enterprise customers. The position blends hands-on coding with technical leadership, taking ownership from early discovery and solution design through build, adoption, and measurable impact, while packaging successful approaches into reusable assets that strengthen future engagements.

Responsibilities - Validate where AI can create business value and surface new opportunities enabled by the technology. - Own end-to-end solution design and system architecture across agents, tools, data, evaluations, controls, human oversight, and deterministic boundaries. - Personally implement critical agents, integrations, evaluation infrastructure, permissions, observability, controls, and feedback loops. - Lead implementation through adoption, setting engineering quality, production readiness, delivery rhythm, and accountability across the project team. - Convert successful work into shared tools, datasets, evaluations, patterns, and engineering methods. - Coach engineers through code review, feedback, and example, and guide senior client stakeholders through difficult technical decisions.

Requirements - Track record of exceptional technical leadership that produced real business impact, including experience leading small engineering teams. - Hands-on experience designing, shipping, and operating production-grade AI systems with sound judgment across architecture, evaluation, observability, reliability, and cost. - Demonstrated ability to keep pace with AI advances, test what matters, and routinely use agentic AI in day-to-day work. - Repeated success owning complex stakeholder environments and linking technical decisions to business value, risk, and organizational change. - Strong coding skills in languages such as Python, TypeScript/JavaScript, or comparable stacks, with first-principles engineering judgment for AI-native systems. - Strong statistical and experimental judgment, including building instrumentation, annotation, review, and feedback loops for uncertain AI behavior.

Nice to have - Operating AI in security-sensitive or regulated environments. - Creating AI tooling, evaluation infrastructure, data assets, or platforms that serve multiple projects. - Hiring, onboarding, and developing engineering talent. - Meaningful open-source contributions or active participation in the AI-engineering community.

Benefits and work setup - Remote position open to candidates located in the EU. - Latitude to choose models, tools, and architecture that fit the problem. - Broad influence over engineering culture and ways of working beyond a single engagement.