Distinguished Engineer - Risk Tech (remote - Eligible)

Capital One Capital One · Banking · McLean, VA +3 · Remote

Distinguished Engineer role focused on applying AI/ML to risk management and the developer lifecycle within an enterprise setting. The role emphasizes technical leadership, strategy, and building intelligent, data-driven risk products. While AI/ML is a key component, the primary output is the risk management product itself, not foundational AI research or model serving infrastructure.

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

  • Software engineering
  • solution architecture
  • Enterprise architecture
  • design patterns
  • Cloud computing (AWS, Microsoft Azure, Google Cloud)
  • Data architecture
  • Event Driven architectures
  • Real-Time architectures

Nice to have

  • Computer Science
  • Java
  • Python
  • Go
  • JavaScript/TypeScript
  • Swift
  • full lifecycle of system development
  • applying Artificial Intelligence or Machine Learning concepts to engineering challenges
  • anomaly detection
  • test optimization
  • intelligent testing
  • Site Reliability Engineering (SRE) principles
  • chaos engineering
  • advanced Observability tooling
  • OpenTelemetry
  • Prometheus
  • Tracing
  • implementing Artificial Intelligence or Artificial Intelligence-enabled solutions

What the JD emphasized

  • Artificial Intelligence or Machine Learning concepts to engineering challenges
  • Artificial Intelligence or Artificial Intelligence-enabled solutions

Other signals

  • AI and automated code transformation into the developer lifecycle
  • data-driven tools that use machine learning to prevent risks & automatically detect issues
  • real-time and intelligent risk management products that are powered by some of the advanced technology (such as Artificial Intelligence)