Lead Machine Learning Engineer (python, Aws, Sql, Genai) (enterprise Platforms Technology)

Capital One Capital One · Banking · New York, NY

Lead Machine Learning Engineer responsible for productionizing ML applications and systems at scale, focusing on architectural design, code development, and ensuring high availability and performance. The role involves designing, building, and delivering ML models, optimizing ML infrastructure, developing data pipelines, and maintaining/monitoring models in production using cloud-based architectures and CI/CD best practices. Emphasis on responsible and explainable AI.

What you'd actually do

  1. Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
  2. Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).
  3. Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
  4. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
  5. Retrain, maintain, and monitor models in production.

Skills

Required

  • Python
  • AWS
  • SQL
  • GenAI
  • designing and building data-intensive solutions using distributed computing
  • programming with Python, Scala, or Java
  • building, scaling, and optimizing ML systems

Nice to have

  • Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
  • building production-ready data pipelines that feed ML models
  • scikit-learn
  • PyTorch
  • Dask
  • Spark
  • TensorFlow
  • developing performant, resilient, and maintainable code
  • data gathering and preparation for ML models
  • people leader experience
  • leading teams developing ML solutions using industry best practices, patterns, and automation
  • developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
  • ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
  • leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • design, build, and/or deliver ML models and components
  • retrain, maintain, and monitor models in production
  • leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
  • construct optimized data pipelines to feed ML models
  • Responsible and Explainable AI

Other signals

  • productionizing machine learning applications and systems at scale
  • develop and review model and application code
  • ensure high availability and performance of our machine learning applications
  • design, build, and/or deliver ML models and components
  • retrain, maintain, and monitor models in production
  • leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
  • construct optimized data pipelines to feed ML models
  • leverage continuous integration and continuous deployment best practices
  • ensure all code is well-managed to reduce vulnerabilities
  • models are well-governed from a risk perspective
  • ML follows best practices in Responsible and Explainable AI