About Nebius:
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.
Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.
Nebius is building a high-performance AI cloud platform, and we are looking for a Forward Deployment Engineer to act as the hands-on bridge between our Azure AI platform and requestor or client teams. You will onboard projects using established runbooks and golden paths, support teams through early operations, and channel real-world delivery feedback back into Platform Engineering.
Requirements
- 5–8 years of experience in cloud engineering, platform engineering, DevOps, solution engineering, or technical consulting.
- Strong hands-on experience with Microsoft Azure and the Azure Well-Architected Framework.
- Experience with core Azure AI, machine learning, and platform services.
- Strong experience with Terraform and Infrastructure as Code.
- Experience with CI/CD pipelines using GitHub Actions, Azure DevOps, GitLab CI, or similar.
- Understanding of machine learning model deployment and lifecycle concepts.
- Strong client-facing, consulting, and stakeholder communication skills.
- Intermediate or higher English.
Responsibilities
- Onboard requestor and client projects onto the Azure AI platform using approved runbooks and golden paths.
- Work closely with teams throughout onboarding, go-live, and early operations.
- Apply and adapt Terraform modules and CI/CD pipelines to project workloads.
- Support machine learning workload deployment and lifecycle management on Azure.
- Capture gaps, delivery friction, and feature requests.
- Provide continuous, structured feedback to Platform Engineering.
- Improve runbooks, documentation, and reusable onboarding patterns.
- Uphold security, governance, and compliance guardrails during every onboarding.
Nice to Have
- Azure Machine Learning or Azure AI Foundry.
- AKS, Kubernetes, and containerisation.
- Python scripting and automation.
- Experience with Azure OpenAI, LLM, agent, or RAG workloads.
- Previous solutions engineering, customer engineering, or consulting experience.
Benefits & Perks:
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Equal Opportunity Statement:
Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
If you need accommodations during the application process, please let us know.
Skills Required
- 5-8 years of experience in cloud engineering, platform engineering, DevOps, solution engineering, or technical consulting
- Strong hands-on experience with Microsoft Azure and the Azure Well-Architected Framework
- Experience with core Azure AI, machine learning, and platform services
- Strong experience with Terraform and Infrastructure as Code
- Experience with CI/CD pipelines using GitHub Actions, Azure DevOps, GitLab CI, or similar
- Understanding of machine learning model deployment and lifecycle concepts
- Strong client-facing, consulting, and stakeholder communication skills
- Intermediate or higher English
- Azure Machine Learning or Azure AI Foundry
- AKS, Kubernetes, and containerisation
- Python scripting and automation
- Experience with Azure OpenAI, LLM, agent, or RAG workloads
- Previous solutions engineering, customer engineering, or consulting experience
What We Do
Cloud platform specifically designed to train AI models
