MLOps / DevOps Engineer – AI Infrastructure
New
C
CreatorIQAI infrastructure
Workplace: Remote, Collaborate effectively with international teams across multiple time zonesFull-TimeMiddle
Salary121,000 - 155,000 USD per year
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Job Details
- Experience
- 3+ years of experience in DevOps, Cloud Engineering, Site Reliability Engineering (SRE), or a similar infrastructure-focused role; 2+ years of hands-on experience supporting production ML/AI infrastructure; 2+ years of hands-on experience with Databricks and/or AWS SageMaker; 2+ years of hands-on experience with AWS services; 2+ years of experience working with containerized environments and orchestration platforms
- Required Skills
- AWSPythonJenkinsKubernetesLinuxTerraformDatabricks
Requirements
- Have 3+ years of experience in DevOps, Cloud Engineering, Site Reliability Engineering (SRE), or a similar infrastructure-focused role.
- Have 2+ years of hands-on experience supporting production ML/AI infrastructure, including model serving, training pipelines, and model monitoring.
- Have hands-on production experience with Databricks and/or AWS SageMaker, including the listed platform components.
- Have 2+ years of hands-on experience with AWS services such as EC2, S3, RDS, Lambda, IAM, VPC, SQS, or API Gateway.
- Have 2+ years of experience with containerized environments and orchestration platforms such as Kubernetes and Amazon EKS.
- Have strong experience building and maintaining CI/CD pipelines with GitLab CI/CD or Jenkins.
- Have hands-on Infrastructure as Code experience with Terraform, Terragrunt, CloudFormation, or similar technologies.
- Have strong Linux system administration and troubleshooting skills.
- Understand networking fundamentals, including routing, load balancing, and network security.
- Have scripting experience with Python, Bash, or similar languages for infrastructure and operational automation.
Responsibilities
- Support and maintain scalable, highly available, and secure cloud infrastructure.
- Provision and manage cloud resources using Infrastructure as Code tools.
- Implement cloud security practices, including access controls, encryption, and vulnerability management.
- Maintain CI/CD pipelines and improve deployment reliability, including zero-downtime deployment strategies.
- Automate infrastructure provisioning, configuration management, and operational processes.
- Operate and scale ML platform infrastructure, model-serving systems, and inference workloads.
- Maintain infrastructure monitoring for model drift, data quality, inference performance, and serving health.
- Maintain monitoring, logging, metrics, and alerting; support incident response and root cause analysis.
- Partner with engineering, QA, Product Support, and Security teams on deployment workflows and cloud operations.
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