Distinguished Engineer (remote - Eligible)

Capital One Capital One · Banking · New York, NY +3 · Remote

Distinguished Engineer role focused on embedding AI and automated code transformation into the developer lifecycle within the Risk Technology organization. The role involves building data-driven tools using machine learning for risk prevention and issue detection, with data powering ML models and regulatory filings. The position emphasizes technical leadership, innovation, mentoring, and full-stack development with a product engineering mindset.

What you'd actually do

  1. Articulate and evangelize a bold technical vision for embedding AI and automated code transformation into the developer lifecycle
  2. Decompose complex problems into practical and operational solutions
  3. Ensure the quality of technical design and implementation
  4. Serve as an authoritative expert on non-functional system characteristics, such as performance, scalability and operability
  5. Continue learning and injecting advanced technical knowledge into our community

Skills

Required

  • Bachelor's Degree
  • 7 years of experience in Software engineering and solution architecture
  • 7 years of experience in Enterprise architecture and design patterns
  • 5 years of experience in Cloud computing (AWS, Microsoft Azure, Google Cloud)
  • 5 years of experience in Data architecture including Event Driven and Real-Time architectures

Nice to have

  • Bachelors' or Master's Degree in Computer Science or a related field
  • 10+ years of professional experience coding in commonly used languages like Java, Python, JavaScript/TypeScript
  • Deep understanding of the SDLC and systems design that accounts for highly reliable systems and highly maintainable systems
  • Experience leading teams to improve their testing and deployment processes, and experience enhancing data products to CI/CD standards
  • Experience in applying Artificial Intelligence or Machine Learning concepts to engineering challenges (e.g., anomaly detection, test optimization, intelligent testing)
  • Deep practical knowledge of Site Reliability Engineering (SRE) principles, chaos engineering, and advanced Observability tooling (e.g., OpenTelemetry, Prometheus, Tracing)

What the JD emphasized

  • Risk Technology organization
  • data-driven tools that use machine learning to prevent risks & automatically detect issues
  • data sets power machine learning models and feed directly into regulatory filings
  • applying Artificial Intelligence or Machine Learning concepts to engineering challenges

Other signals

  • Articulate and evangelize a bold technical vision for embedding AI and automated code transformation into the developer lifecycle
  • We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our business, our customers, or our communities.
  • The data sets power machine learning models and feed directly into regulatory filings.