Cloud Native Machine Learning Architect

5 days ago


Chicago, Illinois, United States United Airlines Full time

United Airlines is a leading airline with a rich history of innovation and commitment to excellence. As a Cloud Native Machine Learning Architect, you will be part of our cutting-edge Digital Technology team that designs, develops, and maintains technology solutions to support the growth of the company.

Salary: $180,000 - $250,000 per year (depending on experience)

We are seeking a talented individual with 7+ years of software engineering experience in machine learning, deep learning, and natural language processing. The ideal candidate will have a strong technical leadership background, familiarity with data science methodologies and frameworks, and experience in building and deploying production ML pipelines.

Key Responsibilities:

  • Design, develop, and deploy cloud-native machine learning infrastructure and services to enable rapid innovation across United Airlines
  • Set up containers and Serverless platform with cloud infrastructure
  • Develop tools and apps to enable ML automation using AWS ecosystem
  • Build data pipelines to enable ML models for batch and real-time data
  • Hands-on development expertise of Spark and Flink for both real-time and batch applications
  • Support large-scale model training and serving pipelines in distributed and scalable environments
  • Stay aligned with the latest developments in cloud-native and ML ops/engineering and experiment with new technologies

Benefits:

  • Competitive salary and benefits package
  • Opportunity to work with cutting-edge technology and contribute to the growth of the company
  • Diverse and inclusive work environment
  • Professional development opportunities

Requirements:

  • Bachelor's degree in Computer Science, Data Science, or related field
  • 7+ years of software engineering experience in machine learning, deep learning, and natural language processing
  • Strong technical leadership and familiarity with data science methodologies and frameworks
  • Experience in building and deploying production ML pipelines


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