Senior Machine Learning Solutions Engineer

6 days ago


Frankfort, Kentucky, United States Ford Motor Company Full time

Ford Motor Company is a global leader in the automotive industry, committed to innovation and excellence. We're seeking an experienced Lead MLOps Cloud Architect to join our team in this leadership role. This position will play a crucial part in shaping the future of our Risk Analytics landscape on Google Cloud.

About the Job

The Lead MLOps Cloud Architect will be responsible for developing an in-depth understanding of the current Risk Analytics landscape and leading the Future State Architecture for the new Risk Platform in GCP. The ideal candidate will have 10+ years of relevant work experience in solution, application, and data architecture, with a deep understanding of cloud hosting concepts and implementations.

Salary and Benefits

We offer a competitive salary range of $140,000 - $200,000 per year, depending on experience. Our comprehensive benefits package includes immediate medical, dental, vision, and prescription drug coverage, flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care, and more.

Key Responsibilities

The Senior Machine Learning Solutions Engineer will be responsible for:

  • Leveraging cloud hosting concepts and implementations to architect solutions for ML-based models and rules engines.
  • Migrating legacy applications to Google Cloud platform and driving business adoption of these platforms.
  • Collaborating with program managers to plan iterations, backlogs, and dependencies across all workstreams to progress the program at the required pace.
Requirements

To be successful in this role, you'll need:

  • Bachelor's degree in computer science or related field.
  • 10+ years of relevant work experience in solution, application, and data architecture, with deep understanding of cloud hosting concepts and implementations.
  • 5+ years of hands-on experience in Risk Analytics, MLOps, and architecting solutions for ML-based models and rules engines.
  • Experience in migrating legacy applications to Cloud platform and business adoption of these platforms.
  • Knowledge of enterprise frameworks and technologies, including strong architecture design patterns, secure interoperability standards, and methods, architecture tools, and processes.


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