MLOps / DevOps Engineer – AI Infrastructure

CreatorIQ is hiring an MLOps / DevOps Engineer to support and improve its cloud and ML/AI infrastructure, automate deployments, and maintain CI/CD pipelines. The role focuses on secure, reliable, scalable infrastructure and production AI/ML platform operations, working with engineering, QA, Product Support, and Security teams.

Responsibilities:

• 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
• 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
• 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
• 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
• 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

Requirements:

• 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 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 to automate infrastructure and operational tasks
• 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 (beyond basic usage) 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