MLOps / DevOps Engineer – AI Infrastructure

The role

The DevOps Engineer supports and improves cloud and ML/AI infrastructure. This role involves automating deployments, maintaining CI/CD pipelines, and managing cloud environments and Infrastructure as Code.

What you will do

• Support and maintain scalable, highly available, and secure cloud infrastructure.
• Provision and manage cloud resources using Infrastructure as Code.
• Implement cloud security best practices.
• Support containerized environments and orchestration platforms.
• Apply DevSecOps principles across infrastructure and deployment workflows.
• Participate in disaster recovery planning, testing, and recovery activities.
• Maintain and optimize CI/CD pipelines.
• Automate configuration management, infrastructure provisioning, and routine operational processes.
• Design, deploy, operate, and secure infrastructure supporting AI and agentic products.
• Operate and scale ML platform infrastructure including Databricks and Sagemaker clusters.

Required qualifications

• 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, including model serving, training pipelines, and model monitoring.
• 2+ Hands-on experience with Databricks (clusters, jobs, ML pipelines, Model Serving) and/or AWS SageMaker (endpoints, inference, model deployment, provisioned throughput, autoscaling) in a production
• 2+ years of hands-on experience with AWS services such as EC2, S3, RDS, Lambda, IAM, VPC, SQS, API Gateway, or similar services.
• 2+ years of experience working with containerized environments and orchestration platforms such as Kubernetes and Amazon EKS.
• Strong experience building and maintaining CI/CD pipelines using tools such as GitLab CI/CD or Jenkins.
• Hands-on experience with Infrastructure as Code using Terraform, Terragrunt, CloudFormation, or similar technologies.
• Strong Linux system administration and troubleshooting skills.
• Solid understanding of networking fundamentals, including routing, load balancing, network security, and related concepts.
• Scripting experience with Python, Bash, or similar languages.

Nice to have

• Experience with Google Cloud, particularly for candidates who have worked across multi-cloud environments.
• Familiarity with Helm and service mesh technologies such as Istio, Linkerd, Traefik, or similar tools.
• Experience designing serverless and event-driven architectures using AWS Lambda, API Gateway, and SQS.
• Exposure to cloud and infrastructure security practices, including vulnerability management and tools such as Nessus, Prowler, Trivy, or firewalls.
• Knowledge of security standards, compliance requirements, and cloud security best practices.
• Experience with observability, log analysis, and monitoring platforms such as Coralogix, Prometheus, Grafana, or similar solutions.
• FinOps experience, including cloud cost monitoring, optimization, and accountability practices.
• Experience with API gateway or API management platforms such as Kong or Apigee.
• Hands-on experience using AI tools to improve engineering workflows, automation, troubleshooting, or agentic use cases.

Benefits

• Learning platform for training and tools.
• 15 days of vacation, floating and company holidays.
• Wellness benefits.
• Paid parental leave.
• Comprehensive medical, dental, vision, life, and disability insurance.
• 401(k) plan.
• Work from home stipend.

About the company

CreatorIQ is enterprise infrastructure for creator marketing, trusted by more than 1,300 of the world's leading brands and agencies. The company has headquarters in Los Angeles and offices in Austin, London, Manila, New York, San Francisco, and Warsaw.