Hover or tap a row for full statistics (EUR / month on this chart).
Salary analysis
Compared with the selected benchmark ("All roles in Remote - Europe"), this listing's salary midpoint is about 94% lower. The offer sits below the benchmark range (€1,692–€11,759). The listed pay band (€307–€500) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 22 comparable listings.
| Market | Lower bound (25th percentile) | Median | Upper bound (75th percentile) |
|---|---|---|---|
| All roles in Remote - Europe | €1,692/per month | €4,842/per month | €11,759/per month |
| Pay in our data — not quoted in ad (Mid-Level) | €307/per month | €404/per month | €500/per month |
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.
Job Details
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
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
Skills & Technologies
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