Senior Production ML Engineer: Scalable AI Pipelines in Winnipeg

Senior Production ML Engineer: Scalable AI Pipelines in Winnipeg

Winnipeg Full-Time 135000 - 165000 $ / year (est.) On-site
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At a Glance

  • Tasks: Design and build scalable ML and AI systems for market intelligence.
  • Company: Join Autodesk, a leader in innovative technology solutions.
  • Benefits: Competitive salary, flexible work options, and opportunities for growth.
  • Other info: Dynamic team with a focus on innovation and career advancement.
  • Why this job: Make an impact by developing cutting-edge AI solutions in a collaborative environment.
  • Qualifications: Experience in machine learning, data engineering, and strong problem-solving skills.

The predicted salary is between 135000 - 165000 $ per year.

Autodesk is seeking a Machine Learning Engineer to design, build, and operationalize production-grade ML and AI systems for the Go-to-market intelligence function.

You will collaborate with data teams and stakeholders to deliver scalable data products, focusing on observability, governance, and cost efficiency.

The role emphasizes end-to-end ML pipelines, model evaluation frameworks, and production deployment with strong engineering discipline.

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Senior Production ML Engineer: Scalable AI Pipelines in Winnipeg employer: Autodesk

Autodesk is an exceptional employer that fosters a dynamic and inclusive work culture, offering flexible hybrid or remote work options for the AI Compute Strategy Specialist role. Employees benefit from continuous growth opportunities through collaboration with innovative teams, while contributing to impactful projects in the AI space. With a focus on analytics and financial forecasting, Autodesk empowers its workforce to drive meaningful change in various sectors, making it a rewarding place to advance your career.

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Contact Details:

Autodesk Recruitment Team

We think you need these skills to ace Senior Production ML Engineer: Scalable AI Pipelines in Winnipeg

Machine Learning
AI Systems Design
Production Deployment
End-to-End ML Pipelines
Model Evaluation Frameworks
Data Collaboration
Observability