MLOps / DevOps Engineer – AI Infrastructure

CreatorIQ is the operating system for creator-led growth trusted by more than 1,300 global brands and agencies.

We’re on a mission to make businesses more human, and humans more impactful. We operate by our values — be intentional, pursue excellence every day, embrace the journey together, and be a good human — every day. CreatorIQ has earned the title of best companies to work for in multiple programs, including BuiltIn LA and NY. It’s been named a Fastest-Growing Company in North America on the Deloitte Technology Fast 500™ for four years, was named a leader in IDC MarketScape: Worldwide Influencer Marketing Platforms for Large Enterprises in 2025, was named a Leader by The Forrester New Wave™: Influencer Marketing Solutions, and has been consistently recognized by G2 as a Leader, and is rated 5 stars on Influencer MarketingHub. We operate in a flexible work model that combines both in-person and remote work to boost collaboration, enhance innovation, and adapt to individual work styles.

We’re seeking passionate, innovative minds to join our journey. Be a part of our dynamic team and let’s transform the industry together!

MLOps / DevOps Engineer – AI Infrastructure

The DevOps Engineer is responsible for supporting and improving cloud and ML/AI infrastructure, automating deployments, and maintaining CI/CD pipelines to ensure efficient, secure, and scalable development workflows. This role plays a crucial part in infrastructure automation, monitoring, and cloud security while collaborating with Software and ML Engineers, Product support, QA, and Security teams.
As a key member of the DevOps team, the DevOps Engineer helps manage cloud environments, CI/CD pipelines, and Infrastructure as Code (IaC), ensuring high availability and compliance with security best practices.

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In this role, you’ll get to:

Cloud Infrastructure, Security & Reliability

• Support and maintain scalable, highly available, and secure cloud infrastructure in accordance with company policies and standards.
• Provision and manage cloud resources using Infrastructure as Code (Terraform, Terragrunt, CloudFormation).
• Implement cloud security best practices, including IAM/role-based access controls, encryption, vulnerability management, and secure infrastructure configurations.
• Support containerized environments and orchestration platforms.
• Apply DevSecOps principles across infrastructure and deployment workflows.
• Participate in disaster recovery planning, testing, and recovery activities.

CI/CD, Automation & Deployment

• Maintain and optimize CI/CD pipelines using tools such as GitLab CI/CD and Jenkins, supporting application and ML model deployments.
• Improve deployment reliability and support zero-downtime deployment strategies.
• Automate configuration management, infrastructure provisioning, and routine operational processes.
• Troubleshoot deployment and pipeline issues and implement improvements to prevent recurrence.
• Develop scripts and automation to reduce manual work and improve engineering efficiency.

AI & Agentic Infrastructure

• Help design, deploy, operate, and secure infrastructure supporting AI and agentic products, including MCP, agents, integrations, internal tooling, and customer-facing use cases.
• Use AI-assisted engineering tools, coding copilots, and AI-driven troubleshooting to improve DevOps productivity and reduce repetitive operational work.
• Evaluate and adopt practical AI-enabled workflows that improve infrastructure management, troubleshooting, and operational efficiency.

MLOps & ML Platform Infrastructure

• Operate and scale ML platform infrastructure, including Databricks and Sagemaker clusters, jobs compute, ML pipelines, and Model Serving endpoints.
• Manage production model-serving infrastructure, including compute capacity, provisioned throughput, and autoscaling for high-throughput inference workloads.
• Maintain infrastructure-level monitoring for model drift, data quality, inference performance, and serving health, while partnering with ML Engineering on model evaluation, quality thresholds, and model correctness.
• Partner with ML Engineering to support reliable CI/CD and production deployment of ML models.

Observability, Incident Response & Engineering Collaboration

• Maintain monitoring, logging, metrics, and alerting solutions using tools such as Prometheus, Grafana, Coralogix, and CloudWatch.
• Support incident response an…