Azure ML/MLOps Engineer for AI Platforms

Perfict Global; Sacramento (CA); On-site; USD 120,000 - 200,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Fun culture

Job summary

Perfict Global is seeking an Azure AI/Client Ops engineer to design data platform and pipelines for advanced analytics. You will translate prototypes into scalable production systems and partner with data scientists and engineers.

Responsibilities include deploying AI components with Azure services, MLflow, Kubeflow, and orchestrating ML pipelines with emphasis on automation, monitoring, and security in production environments.

Qualifications

• Bachelor's degree in Information Systems; 5+ years of development experience.
• Travel up to 25%.
• 3–6 years managing ML projects end-to-end, last 18 months in MLOps.
• Strong programming skills in Python, Java, or Scala.
• Experience with ML libraries/frameworks: TensorFlow, PyTorch, scikit-learn.
• Experience with Docker and Kubernetes.
• Azure cloud platform experience.
• Automation of ML model deployment.

Responsibilities

• Design, create, and deploy AI/Client applications using Azure tools.
• Integrate AI components into data workflows with data scientists and engineers.
• Implement NLP and ML models, deploy to production.
• Automate deployment and monitoring of AI models with DevOps.
• Use AI to automate processes (sentiment analysis, image ID, recommendations, chatbots).
• Build ML pipelines and scale Client models in production.
• Automate CI/CD for data, code, and model changes.
• Monitor model performance and update as needed.
• Ensure security and compliance of ML systems.
• Collaborate with data scientists to optimize models.

Skills

Python

Java

Scala

Azure

TensorFlow

PyTorch

scikit-learn

Education

Bachelor's degree in Information Systems

Tools

Docker

Kubernetes

MLflow

Kubeflow

Job description

Perfict Global is seeking an Azure AI/Client Ops engineer to design data platform and pipelines for advanced analytics. You will translate prototypes into scalable production systems and partner with data scientists and engineers.

Responsibilities include deploying AI components with Azure services, MLflow, Kubeflow, and orchestrating ML pipelines with emphasis on automation, monitoring, and security in production environments.