MLOps / DevOps Engineer – AI Infrastructure

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