Partner Solutions Architect

We are seeking a Partner Solutions Architect to serve as the technical interface between Nebius and our strategic technology partners, ranging from data platforms and MLOps vendors to AI frameworks and ISVs that build on GPU infrastructure. This is a hands-on engineering and integration role. You will design and develop integrated solutions, build reference implementations, enable partner engineering teams, and ensure joint customers succeed with combined offerings. You will influence Nebius’s product roadmap and drive deep technical collaboration across partner ecosystems. You’re welcome to work from our office in Amsterdam or remotely from any EU country. Your responsibilities will include: Own the technical relationship with strategic partners as the primary interface to Nebius engineering and product; Design and architect high-impact integration solutions, delivering clear joint customer value; Build and maintain reference implementations and production-grade integrations; Define enterprise-ready integration patterns (networking, security, compliance, observability); Translate partner and customer feedback into actionable product and roadmap requirements; Enable partner teams through technical documentation, training, and demo environments; Provide technical leadership in joint customer deployments, architecture reviews, and complex troubleshooting. We expect you to have: 5+ years in solutions architecture, partner engineering, or technical pre-sales at cloud or data platform companies; Strong expertise in cloud infrastructure; Hands-on experience with GPU-accelerated AI/ML workflows (training, inference, MLOps); Proven background in integration architecture (API design, data pipelines, security, observability); Experience with Python and IaC tools; Excellent communication skills with the ability to engage technical and non-technical stakeholders. It will be an added bonus if you have: Track record of building or scaling partner technical programs or ecosystems; Experience working with or at Snowflake, Databricks, AWS, Azure, or GCP; Background in ML lifecycle (training pipelines, fine-tuning, inference optimization, RAG).