Senior Azure AI / MLOps Engineer

New

Flexible remote work opportunities within the United States.Full-TimeSenior

Salary not disclosed

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Job Details

Strong experience designing and managing Azure-based AI, machine learning, and cloud infrastructure solutions.
Proven expertise in MLOps practices including model deployment, monitoring, CI/CD automation, and machine learning lifecycle management.
Hands-on experience with Azure services such as Azure Machine Learning, Azure DevOps, Azure Kubernetes Service (AKS), Azure Data Factory, and related cloud technologies.
Proficiency in scripting and programming languages commonly used in AI and cloud engineering environments such as Python, Bash, or PowerShell.
Experience with containerization and orchestration technologies including Docker and Kubernetes.
Strong understanding of DevOps methodologies, infrastructure-as-code practices, and cloud automation frameworks.
Familiarity with machine learning workflows, data engineering concepts, and scalable AI infrastructure design.
Excellent analytical, troubleshooting, and problem-solving skills with the ability to manage complex technical environments.
Strong communication and collaboration abilities, with experience working in distributed or cross-functional teams.
Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.

Design, implement, and maintain scalable AI and MLOps solutions within Azure cloud environments to support enterprise machine learning initiatives.
Develop and optimize CI/CD pipelines, infrastructure automation, and deployment workflows for machine learning models and AI-driven applications.
Collaborate with data scientists, software engineers, cloud architects, and business stakeholders to operationalize AI solutions and improve model lifecycle management.
Build and manage cloud-native infrastructure using Azure services, ensuring reliability, scalability, performance, and security across AI platforms.
Monitor machine learning environments and production systems to ensure model performance, operational stability, and continuous improvement.
Support containerization, orchestration, and automation efforts using modern DevOps and cloud engineering practices.
Contribute to architecture discussions, technical documentation, and engineering best practices related to AI operations and cloud infrastructure.
Evaluate emerging technologies, AI frameworks, and Azure services to enhance platform capabilities and improve engineering efficiency.
Troubleshoot complex technical issues related to cloud infrastructure, AI deployment, integrations, and operational workflows.

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