Requirements

Expected technologies

Kubernetes

Docker

GitHub

Grafana

Python

Azure DevOps

Our requirements

• 7–14 years of experience in DevOps, Platform Engineering, or Cloud Infrastructure roles
• Strong expertise with Microsoft Azure, including Azure Machine Learning, AKS, Azure DevOps, and serverless services
• Advanced proficiency in Infrastructure as Code with Terraform and Pulumi
• Proven experience designing and scaling containerized, microservices-based architectures
• Deep understanding of CI/CD pipelines, automation, and quality gates
• Strong scripting skills in Python, Bash, and PowerShell
• Hands-on experience with Docker, Kubernetes, and Helm
• Knowledge of monitoring and observability tools such as Prometheus, Grafana, ELK Stack, and Azure Monitor

Optional

• Familiarity with Machine Learning & AI, including ML lifecycle, TensorFlow/PyTorch, and MLOps tools (MLflow, Kubeflow, Azure ML)
• Strong DevSecOps background — security scanning, compliance, and tools like Twistlock, Aqua Security, Azure Security Center
• Experience with multi-cloud environments (AWS, GCP)
• Knowledge of GitOps workflows (ArgoCD, Flux)
• Understanding of data engineering and big data tools (Spark, Databricks)
• Azure certifications (Solutions Architect, DevOps Engineer Expert) or equivalent
• Familiarity with service mesh technologies (Istio, Linkerd)

Your responsibilities

• Design, implement, and maintain scalable MLOps pipelines for model training, validation, deployment, and monitoring
• Build and manage cloud infrastructure using Infrastructure as Code tools (Terraform, Pulumi) with a focus on Azure services
• Implement horizontal and vertical scaling solutions using Azure Kubernetes Service (AKS), Container Registry, and App Services
• Design, setup, execute, and debug CI/CD pipelines for ML model deployment and infrastructure provisioning
• Collaborate with data scientists and ML engineers to operationalize machine learning models at scale
• Monitor model performance, data drift, and system health using observability tools and implement automated remediation
• Optimize infrastructure costs while maintaining high availability and performance standards
• Establish and enforce best practices for ML model versioning, experimentation tracking, and reproducibility
• Implement security best practices and compliance requirements across AI/ML infrastructure
• Provide technical guidance and mentorship to junior team members
• Stay current with emerging MLOps tools, platforms, and industry best practices