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

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 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.

Nice to have:

• 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 practic