About The Position
Jedox is seeking an AI & Automation Cloud Infrastructure Engineer to shape and develop its in-house AI cloud platform. This role focuses on enabling expandable, protected, and automated AI, ML, and GenAI workloads. The engineer will be responsible for designing, building, and operating robust cloud infrastructures, defining standards, and ensuring operational excellence. Collaboration with Cloud Platform, SRE, Architecture, and Engineering teams is key to delivering reliable, cost-efficient, and cloud-native AI services that drive Jedox's innovation.
Requirements
- 3+ years' experience in cloud engineering, platform, DevOps, or SRE roles, with exposure to AI platform enablement.
- Strong hands-on experience with cloud platforms (ideally Microsoft Azure), including networking, identity, storage, and security.
- Practical experience with Kubernetes and containerized workloads (AKS preferred).
- Solid understanding of AI/ML workloads, including training vs. inference, scaling behavior, and resource requirements.
- Hands-on experience with automation, GitOps, and MLOps concepts (e.g., CI/CD, model lifecycle, Argo CD, and Helm).
- Experience with observability, reliability, and cost-aware cloud operations (e.g., monitoring stacks and FinOps basics).
- Strong software engineering mindset, with the ability to build maintainable automation, tools, or services.
- Highly motivated to continuously learn and develop skills.
- Excellent written and verbal English communication skills with strong attention to detail.
Responsibilities
- Design and develop strong architectures for infrastructure, data, security, and integration patterns for AI workloads.
- Define reusable templates, standards, and architectures for efficient cloud-based deployment of AI, ML, and GenAI workloads.
- Collaborate with Cloud Platform, SRE, Architecture, and Engineering teams to ensure scalable and reliable AI services.
- Promote controlled environment PR-based processes for models, services, and infrastructure using GitOps workflows.
- Contribute to the MLOps lifecycle, including model deployment, promotion, rollback, and operational support.
- Design for scalability, performance, and high availability in production environments to ensure maximum efficiency and reliability.
- Enhance monitoring, incident response, and ensure compliance, data protection, and best practices.
- Improve observability, reliability, and security by enhancing monitoring and incident response.
- Ensure compliance, data protection, and best practices by enhancing monitoring and incident response.
- Contribute to AI platform roadmaps and serve as a subject-matter expert for AI cloud technologies.
Benefits
- Comprehensive health benefits plans
- Pension plans
- Generous vacation time
- Flexible work options
- Opportunities for personal and professional development, including internal & external training and certifications
- Corporate discounts across many brands and products
- Public transit reimbursement or other perks like bike leasing
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
