xis.ai, a spin-off from XRAY-LAB, aims to revolutionize industrial quality inspection and process monitoring using Artificial Intelligence. The company's technology enables industries to accuratelyโฆ
xis.ai, a spin-off from XRAY-LAB, aims to revolutionize industrial quality inspection and process monitoring using Artificial Intelligence. The company's technology enables industries to accuratelyโฆ
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We are looking for a
DevOps Engineer
who is passionate about automation, cloud infrastructure, and building scalable, reliable systems. The ideal candidate has solid hands-on experience with
AWS
,
Kubernetes
,
CI/CD
,
networking fundamentals
, and a strong understanding of modern DevOps practices.
Experience with
MLOps
is a plus, but not required.
If you enjoy solving complex cloud problems, improving deployment pipelines, and working with cross-functional engineering teams, we want to meet you!
๐ง Responsibilities
๐ Required Skills
1-3 years of experience as a
DevOps / Cloud Engineer
โญ Nice-to-Have Skills
Experience with
MLOps tools
such as MLflow, Kubeflow, SageMaker, or DVC
Understanding of model deployments (Triton, BentoML, Seldon, etc.)
Knowledge of data pipelines or ETL frameworks
Security best practices (IAM, KMS, Secrets Manager)
๐ฏ What We Offer
Competitive salary
Fast-paced environment with modern tech stack
Freedom to innovate and optimize infrastructure
Opportunities to work on AI/ML-driven systems
Design, manage, and automate
AWS cloud infrastructure
Deploy and maintain
Kubernetes
clusters for scalable applications
Build and optimize
CI/CD pipelines
(GitHub Actions, GitLab, Jenkins, etc.)
Implement monitoring, logging, and alerting systems (CloudWatch, Prometheus, Grafana)
Improve system reliability, uptime, and deployment quality
Manage VPCs, load balancers, DNS, firewalls, routing, and other
core networking components
Develop infrastructure using
IaC tools
such as Terraform or CloudFormation
Collaborate with development and QA teams for smooth, automated deployments
(Optional) Support
MLOps workflows
โ model deployment, ML pipelines, model inference infrastructure
