MLOps / DevOps Engineer – AI Infrastructure

Role details

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. 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 and perform Root Cause Analysis (RCA) for infrastructure and deployment-related issues. - Improve system observability through effective log aggregation, metrics collection, monitoring, and alerting. - Partner with Software Engineers, ML Engineers, QA, and Software Engineers in Test to improve deployment workflows and integrate automated testing into CI/CD pipelines. - Collaborate with IT Security to maintain secure cloud operations and infrastructure policies. - Respond to engineering and Product Support requests in a timely manner and provide technical infrastructure support when needed. - Maintain accurate internal technical and operational documentation. - Collaborate effectively with international teams across multiple time zones WHO YOU ARE AND WHAT YOU’LL NEED FOR THIS POSITION: - 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 environment. - 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