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Nebius is a Nasdaq-listed AI cloud infrastructure company headquartered in Amsterdam, building a full-stack platform for data, model training, and production deployment. The company operates with 1,500+ employees across R&D hubs in Europe, UK, North America, and Israel.
You will join the Token Factory team as a Senior Customer AI Engineer, sitting at the intersection of engineering, delivery, and customer success. Your mission is to help customers transition from proof-of-concept to production and scale AI workloads reliably on Nebius infrastructure.
This is NOT a sales role, pure support role, or solution architect position. Instead, you own the production journey end-to-end: leading PoC-to-production transitions, ensuring workloads are deployed stably and scalably, and driving time-to-value.
Key responsibilities:
- Own the production journey: lead transitions from PoC to production, ensure customer workloads are deployed, stable, and scalable; drive time-to-production and time-to-value.
- Ensure technical success: understand customer architectures and use cases; monitor and improve performance (latency, throughput), cost efficiency, and reliability; identify and resolve bottlenecks proactively.
- Act as trusted technical partner: work directly with customer engineering teams; provide guidance on best practices and optimization; translate technical challenges into actionable solutions.
- Manage risks and incidents: serve as primary technical contact for production issues; coordinate with internal teams to resolve incidents; communicate clearly during high-pressure situations.
- Drive continuous improvement: identify opportunities to optimize and expand usage; provide structured feedback to Product and Infrastructure teams; help shape better solutions based on real customer needs.
You will work closely with customer engineering teams, Solution Architects, and Product/Infrastructure teams to drive stable, performant, and cost-efficient deployments.
REQUIREMENTS:
- Technical background: practical knowledge of inference frameworks (e.g., vLLM, TensorRT, or similar); solid understanding of cloud or infrastructure systems, distributed systems or high-load applications; AI/ML workloads (LLMs, inference, etc.) is a strong plus; ability to troubleshoot and reason about system performance.
- Customer-facing experience: experience working directly with technical customers (e.g., engineers, ML teams); ability to communicate complex topics clearly and effectively.
- Ownership & execution: strong sense of ownership – you drive outcomes, not just tasks; ability to manage multiple customers and priorities; structured, proactive, and solution-oriented mindset.
- Added bonus: experience with GPU workloads or AI infrastructure; background in solutions engineering, SRE, or technical support in B2B environments.
