About this role
The DevOps & AI/ML Infrastructure Engineer is responsible for ensuring the reliability, security, and scalability of CreatorIQ's cloud and machine learning infrastructure. This role centers on automating deployments, managing CI/CD pipelines, and maintaining robust infrastructure as code practices to support the company's platform. The engineer will collaborate closely with Software, ML, Product support, QA, and Security teams to maintain efficient and secure development workflows.
Key responsibilities involve provisioning and managing cloud resources, implementing security best practices, and ensuring high availability across containerized environments. A significant part of the role involves supporting the infrastructure for AI and agentic products, which includes designing and securing the necessary deployment environments. Furthermore, the engineer will operate and scale the ML platform infrastructure, focusing on model serving, monitoring for performance, and maintaining data quality.
This position exists to bridge the gap between development and operational excellence, enabling the team to deploy and manage complex AI/ML systems reliably. Individuals who excel at automation, infrastructure management, and thrive in a collaborative environment, particularly those comfortable applying DevSecOps principles, would be well-suited for this role.
What you will do
- Support and maintain scalable, highly available, and secure cloud infrastructure according to established company policies.
- Provision and manage cloud resources utilizing Infrastructure as Code tools like Terraform, Terragrunt, and CloudFormation.
- Implement cloud security best practices, including IAM, encryption, and vulnerability management across infrastructure.
- Maintain and optimize CI/CD pipelines using tools such as GitLab CI/CD and Jenkins for application and ML model deployments.
- Automate configuration management and infrastructure provisioning processes to enhance engineering efficiency.
- Design, deploy, and secure the infrastructure supporting AI and agentic products, including agents and integrations.
- Operate and scale ML platform infrastructure, such as Databricks clusters and model serving endpoints.
- Maintain monitoring, logging, and alerting solutions to ensure system observability and support incident response.
What you need
- Demonstrated experience supporting and improving cloud and ML/AI infrastructure is required.
- Proficiency in Infrastructure as Code tools such as Terraform, Terragrunt, and CloudFormation is essential.
- Experience implementing cloud security best practices, including IAM controls and encryption, is necessary.
- Solid understanding of CI/CD principles and the ability to maintain pipelines using GitLab CI/CD or Jenkins is required.
- Experience supporting containerized environments and orchestration platforms is a must.
- Familiarity with DevSecOps principles applied to infrastructure and deployment workflows is required.
- Experience operating and scaling ML platform infrastructure, including model serving and compute capacity, is important.
- Ability to troubleshoot deployment and pipeline issues and develop automation scripts to reduce manual work.
Nice to have
- Experience with AI-assisted engineering tools
- Experience with AI-driven troubleshooting tools
Benefits
- Flexible work model combining in-person and remote work
Skills and tech stack
Questions candidates ask
What is the remote work policy?
CreatorIQ operates in a flexible work model that combines both in-person and remote work to boost collaboration, enhance innovation, and adapt to individual work styles.
What is the expected salary range?
The salary for this role is listed between $121K and $155K.
What is the primary tech stack used for infrastructure?
The role utilizes tools such as Terraform, Terragrunt, and CloudFormation for provisioning, alongside CI/CD tools like GitLab CI/CD and Jenkins.
How is collaboration structured within the team?
The DevOps Engineer collaborates with Software Engineers, ML Engineers, QA, and Software Engineers in Test to improve deployment workflows and integrate automated testing into CI/CD pipelines.