Lead Machine Learning Engineer (mlops, Kserve + Building Kubernetes Clusters, Pytorch, Tensorflow on Aws)

Capital One Capital One · Banking · McLean, VA +4

Lead Machine Learning Engineer focused on MLOps, building Kubernetes clusters, and deploying ML models at scale on AWS for a fintech company. The role involves designing, building, and maintaining ML infrastructure and pipelines, collaborating with data science and product teams, and ensuring the performance, reliability, and responsible deployment of ML systems.

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

  • Bachelor’s degree
  • At least 6 years of experience designing and building data-intensive solutions using distributed computing
  • At least 4 years of experience programming with Python, Scala, or Java
  • At least 2 years of experience building, scaling, and optimizing ML systems

Nice to have

  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
  • 3+ years of experience building production-ready data pipelines that feed ML models
  • 3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
  • 2+ years of experience developing performant, resilient, and maintainable code
  • 2+ years of experience with data gathering and preparation for ML models
  • 2+ years of people leader experience
  • 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • Experience 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

What the JD emphasized

  • productionizing machine learning applications and systems at scale
  • building, scaling, and optimizing ML systems
  • building production-ready data pipelines that feed ML models
  • designing, implementing, and scaling complex data pipelines for ML models

Other signals

  • productionizing machine learning applications and systems at scale
  • build and deploy proprietary solutions
  • build and deploy proprietary solutions that are central to our business
  • advance the state of the art in science and AI engineering
  • build and deploy proprietary solutions that are central to our business and deliver value to millions of customers
  • AI models and platforms empower teams across Capital One
  • responsible and scalable ways for the highest leverage impact
  • design, build, and/or deliver ML models and components that solve real-world business problems
  • inform your ML infrastructure decisions using your understanding of ML modeling techniques
  • Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
  • create and enhance software that enables state-of-the-art big data and ML applications
  • 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, including test automation and monitoring, to ensure successful deployment of ML models and application code
  • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
  • At least 2 years of experience building, scaling, and optimizing ML systems
  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance