Full Job Description
.NET MLOps Specialist (ML.NET & Azure ML)
Location: Alpharetta, GA (willing to travel to client locations)
Employment Type: Full-Time (W2)
Role Overview
We are seeking a proficient .NET MLOps Specialist to streamline machine learning workflows using ML.NET and Azure ML. This role focuses on integrating MLOps practices with .NET and C# for efficient model deployment and management.
Key Responsibilities
• Develop machine learning pipelines using ML.NET and C# to train, evaluate, and deploy models.
• Integrate ML models into production systems with Azure ML for scalable inference and monitoring.
• Automate model deployment and updates using MLOps practices and CI/CD pipelines with Azure DevOps.
• Collaborate with data scientists to optimize ML workflows and ensure model accuracy and performance.
• Manage model versioning, governance, and monitoring within .NET applications.
• Ensure security and compliance of ML models in production environments.
Required Qualifications
• Bachelor's degree in Computer Science, Software Engineering, or a related field (or equivalent experience).
• 3 years of experience in .NET and C# development with a focus on machine learning and MLOps.
• Proficiency in building ML pipelines with ML.NET and deploying models using Azure ML.
• Experience with MLOps practices, including model deployment, monitoring, and CI/CD automation.
• Strong understanding of integrating ML models into production applications.
Preferred Qualifications
• Familiarity with Azure DevOps for automating MLOps workflows and pipeline orchestration.
• Exposure to advanced ML frameworks like TensorFlow or PyTorch for integration with .NET applications.
• Knowledge of cloud-based data preprocessing tools like Azure Data Factory for ML pipelines.
Compensation & Benefits
• Competitive salary and comprehensive benefits package (healthcare, PTO, 401k).
• Opportunities for professional growth and upskilling in AI and cloud technologies.
Skills:
.NET, ML.NET, Azure ML, MLOps, Model Deployment, DevOps, C#, Machine Learning