Distinguished Engineer - Risk Tech (Remote - Eligible)

Capital One is a financial services company aiming to redefine banking in the cloud. As a Distinguished Engineer, you will lead technical innovation in risk management, mentor teams, and develop advanced data-driven tools using machine learning to enhance business outcomes.

Responsibilities:

• Articulate and evangelize a bold technical vision for embedding AI and automated code transformation into the developer lifecycle
• Decompose complex problems into practical and operational solutions
• Ensure the quality of technical design and implementation
• Serve as an authoritative expert on non-functional system characteristics, such as performance, scalability and operability
• Continue learning and injecting advanced technical knowledge into our community
• Handle several projects simultaneously, balancing your time to maximize impact
• Act as a role model and mentor within the tech community, helping to coach and strengthen the technical expertise and know-how of our engineering and product community
• Ensure Code is of the highest quality & standard while being an active contributor and reviewer on critical repos of the application
• Develop full stack applications with a product engineering mindset, spanning frontend and backend ecosystems that balance simplicity with flexibility

Requirements:

• Bachelor's Degree
• At least 7 years of experience in Software engineering and solution architecture
• At least 7 years of experience in Enterprise architecture and design patterns
• At least 5 years of experience in Cloud computing (AWS, Microsoft Azure, Google Cloud)
• At least 5 years of experience in Data architecture including Event Driven and Real-Time architectures
• Bachelors' or Master's Degree in Computer Science or a related field
• 10+ years of experience in Software Engineering and solution architecture
• 10+ years of professional experience coding in commonly used languages like Java, Python, Go, JavaScript/TypeScript, Swift, etc
• 8+ years of professional experience in the full lifecycle of system development, from conception through architecture, implementation, testing, deployment and production support
• 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)
• Experience in implementing Artificial Intelligence or Artificial Intelligence-enabled solutions