Engineering Manager - AI Product (100% Remote EMEA)

About the role

A European technology company operating a cloud and infrastructure platform is looking for an AI Engineering Manager to lead its small, high-leverage team behind the AI product. The role covers a team of AI engineers and researchers building the systems powering AI-enabled products and the model inference offering. It is a hybrid people and technical leadership position, fully remote within the European time zone, with an English-speaking team.

Key responsibilities

• Manage, coach and grow a team of AI/ML engineers and researchers, including hiring, career development and performance management.
• Own delivery of AI-enabling projects across the product portfolio, turning ambiguous goals into scoped, sequenced engineering plans.
• Co-own the roadmap and operations of the inference offering, including reliability, latency, cost and scaling of model serving infrastructure.
• Partner with research scientists to move promising models and techniques from experimentation into production.
• Set engineering standards for code quality, experimentation, evaluation, on-call and incident response.
• Work with Product, Infrastructure and Data teams to prioritize work and remove blockers.
• Report progress, risks and capacity to engineering leadership and represent the team in planning and roadmap discussions.
• Keep up with AI/ML and inference developments and feed them into the team's technical strategy.

Required profile

• 7+ years in a technical leadership or engineering management role (AI/ML teams preferred), plus a strong track record as a hands-on engineer or researcher.
• Proven experience building and operating AI/ML platform or inference infrastructure in production (model serving, GPU workloads, latency and cost optimization).
• Experience managing both researchers and engineers with different working styles and timelines.
• Excellent communication skills, able to explain technical trade-offs to engineers and non-technical stakeholders.
• Experience owning production systems, including reliability and incident response, is a plus.

Required skills

• Working knowledge of the AI/ML lifecycle: model training or fine-tuning, evaluation and production deployment.
• Fluency in model inference and serving infrastructure, such as GPU scheduling, batching, quantization and serving frameworks like vLLM or Triton.