ML DevOps - Azure

Role & responsibilities :

Job description

We are looking for an experienced DevOps Architect expertise in MLOps to join our team. The ideal candidate must have experience in setting up and managing Machine Learning Operations (MLOps) pipelines. This role involves collaborating with data science, development, and IT operations teams to automate, optimize, and scale machine learning workflows while ensuring seamless integration with existing DevOps processes.
Key Responsibilities:

1. DevOps Practices:
• Implement and manage continuous integration (CI) and continuous delivery (CD) pipelines for application development.
• Automate release processes, configuration management, and environment provisioning.
• Apply Infrastructure as Code (IaC) using tools like Terraform, Ansible, or Azure Resource Manager (ARM) Templates.
• Ensure end-to-end monitoring and logging for applications using tools like Azure Monitor, Log Analytics, Prometheus, Grafana.
2. MLOps Expertise: ( 5 years exp)
• Design, implement, and maintain MLOps pipelines to support data science teams.
• Automate the deployment, retraining, and versioning of machine learning models.
• Integrate model lifecycle management into CI/CD pipelines.
• Collaborate with data engineers to ensure data pipeline consistency for model training and deployment.
• Implement model monitoring to ensure performance, accuracy, and drift detection.
3. Azure DevOps Expertise:
• Manage Azure DevOps Services for source control, build and release pipelines, and project management.
• Optimize the usage of Azure DevOps for enterprise-grade deployments.
• Ensure secure and scalable Azure Kubernetes Service (AKS) for hosting applications and machine learning models.
• Leverage Azure Machine Learning Service to manage and deploy machine learning models at scale.
4. Collaboration & Communication:
• Work closely with data scientists, developers, and IT operations to ensure smooth DevOps and MLOps workflows.
• Provide technical leadership and guidance on best practices for both DevOps and MLOps.
• Develop and implement strategies for collaboration and automation across teams.
5. Security & Compliance:
• Ensure security and compliance in the DevOps pipeline, including CI/CD security practices, secrets management, and vulnerability scanning.
• Apply enterprise security policies and monitor for anomalies in both DevOps and machine learning environments.

Key Skills and Qualifications:

• 8+ years of experience in DevOps engineering, with a focus on Azure DevOps tools and services.
• Experience in MLOps, including deploying and managing machine learning models in production.
• Proficiency in Azure Kubernetes Service (AKS), Docker, and container orchestration.
• Experience with Terraform, Ansible, Chef, Puppet, or ARM templates for infrastructure automation.
• Proficient in Python, PowerShell, or Bash scripting for automation.
• Strong knowledge of CI/CD practices and tools like Jenkins, GitLab, or Azure Pipelines.
• Experience in machine learning lifecycle tools like MLflow, Kubeflow, or Azure Machine Learning.
• Hands-on experience with Azure Data Services (Data Lake, Databricks, Data Factory) for data pipeline integration in MLOps.
• Familiarity with monitoring and logging tools such as Prometheus, Grafana, Azure Monitor.
• Strong problem-solving skills and the ability to work in a fast-paced environment.
• Excellent communication and collaboration skills.

Nice to Have:

• Familiarity with AI/Client model evaluation techniques and integration with business processes.
• Experience with AI frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Exposure to serverless architectures on Azure.