Nebius Group
Nebius Group is building one of the world’s leading AI infrastructure companies, focusing on providing the necessary compute, storage, and tools for developers in the AI space. Based in Europe and listed on Nasdaq, Nebius has a global presence with R&D centers across Europe, North America, and Israel. The company's primary offering is an AI-centric cloud platform designed for intensive AI workloads, complemented by various other businesses involved in generative AI development, edtech, and autonomous technology.
AI infrastructure • Cloud platforms • GPU clusters • GPU cloud • GPUs as a service
AI/ML Specialist - Solutions Architect
February 15
Ansible
Cloud
Docker
Hadoop
Java
Kafka
Kubernetes
Node.js
NoSQL
Python
PyTorch
Scikit-Learn
Spark
SQL
Tensorflow
Terraform
Go
Nebius Group
Nebius Group is building one of the world’s leading AI infrastructure companies, focusing on providing the necessary compute, storage, and tools for developers in the AI space. Based in Europe and listed on Nasdaq, Nebius has a global presence with R&D centers across Europe, North America, and Israel. The company's primary offering is an AI-centric cloud platform designed for intensive AI workloads, complemented by various other businesses involved in generative AI development, edtech, and autonomous technology.
AI infrastructure • Cloud platforms • GPU clusters • GPU cloud • GPUs as a service
📋 Description
• Designing customer-centric solutions that maximize business value and align with strategic goals. • Building and maintaining long-term relationships to foster trust and ensure customer satisfaction. • Delivering technical presentations, producing whitepapers, creating manuals and hosting webinars for audiences with varying technical expertise. • Collaborating with engineering and product teams to effectively prioritize and relay customer feedback.
🎯 Requirements
• 3+ years of experience with cloud technologies in MLOps engineering, Machine Learning engineering or similar roles. • Strong understanding of ML ecosystems, including models, use cases and tooling. • Proven experience in setting up and optimizing distributed training pipelines across multi-node and multi-GPU environments. • Expertise in deploying inference infrastructure for production workloads. • Ability to transition ML pipelines from POC to scalable production systems. • Hands-on knowledge of frameworks like PyTorch or JAX. • Excellent verbal and written communication skills.
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