Job Summary

We are seeking an experienced Site Reliability Engineer with AI MLOps to support the development and optimization of our ERP product, primarily in Azure and Windows environments. This role combines MLOps expertise with Site Reliability Engineering (SRE) principles to ensure the reliable, scalable, and cost-efficient deployment of AI models. The ideal candidate will focus on improving security, compliance, and operational efficiency, collaborating with North American and global teams to meet business objectives.

Key Responsibilities

AI MLOps Pipeline: Build and optimize CI/CD pipelines to automate the training, testing, and deployment of AI models on Azure, with a strong emphasis on improving efficiency and reducing costs.

Azure Infrastructure Management: Manage and maintain scalable, secure infrastructure using Azure services like Azure Machine Learning, AKS, and Virtual Machines. Continuously optimize resource usage and implement cost-saving measures.

Windows Server Management: Oversee Windows-based servers hosted on Azure, ensuring they meet performance, security, and compliance requirements, while also identifying and executing cost-saving opportunities.

Cost Optimization: Analyze and manage infrastructure costs by identifying unused or underused resources and implementing optimization strategies to drive cost savings.

Monitoring & Performance Optimization: Monitor the health, performance, and costs of AI models and services using Azure Monitor, NewRelic and other tools. Identify performance bottlenecks and optimize for both operational efficiency and cost reduction.

Model Versioning & Governance: Assist in managing model version control, governance, and lifecycle processes with a focus on cost-effective operations.

Cross-functional Collaboration: Collaborate with data scientists, AI engineers, and software developers to support the efficient deployment and operationalization of AI models, while actively seeking ways to minimize costs.

Incident Management & Automation: Participate in incident resolution and automate tasks to reduce manual work, improve system reliability, and lower operational overhead.

Security & Compliance Assurance: Ensure AI/ML workloads comply with security and regulatory standards, implementing cost-efficient solutions to enhance security and data protection.

Qualifications

Experience: 2 –5 years in MLOps, SRE, or similar roles, focusing on Azure and Windows environments.

Cloud Skills: Proficient in Azure services, managing infrastructure, and Windows workloads.

SRE Knowledge: Familiar with Site Reliability Engineering principles like monitoring and automation.

DevOps: Hands-on experience with CI/CD tools like Azure DevOps.

Scripting: Skilled in PowerShell and Python for automation.

Containers: Knowledge of Docker and Kubernetes for deploying AI/ML applications.

Windows Admin: Strong experience managing Windows Servers and related services.

AI/ML Knowledge: Understanding of AI/ML workflows and model deployment.

Nice-to-Have

Experience with Infrastructure-as-Code tools like Terraform.

Azure certifications (e.g., Azure AI Engineer, Azure DevOps Engineer)

Experience implementing cost-saving strategies in cloud environments

Soft Skills

Strong problem-solving skills with the ability to troubleshoot complex issues.

Excellent communication skills and the ability to collaborate effectively with cross-functional teams.

A passion for innovation and continuous improvement in AI/ML systems.