Distinguished Engineer, Full Stack (remote Eligible)

Capital One Capital One · Banking · Cambridge, MA +3 · Remote

Distinguished Engineer role focused on driving architectural excellence and establishing AI-native engineering standards within Capital One's Finance Technology teams. The role involves leading proof-of-concept development for emerging AI frameworks, championing AIOps practices, and building LLM-backed or agentic systems. Requires experience in full ML development lifecycle, distributed HPC and ML systems, and cloud computing, with a strong emphasis on hands-on coding and full SDLC ownership.

What you'd actually do

  1. Lead proof-of-concept development for emerging AI frameworks and architectural patterns
  2. Champion AIOps practices — driving platform reliability through AI-assisted anomaly detection, automated remediation, and predictive alerting across the portfolio
  3. Collaborate with other Finance Technology towers reviewing architectures and designs outside your immediate domain and contributing a cross-cutting perspective that raises the bar org-wide
  4. Articulate and evangelize a bold technical vision for your domain
  5. Ensure the quality of technical design and implementation

Skills

Required

  • 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 7 years of experience in designing and building distributed HPC and ML systems
  • At least 7 years of experience in full ML development lifecycle using AI and ML frameworks
  • At least 7 years of experience in cloud computing (AWS, Microsoft Azure, Google Cloud)
  • At least 7 years of experience in data engineering

Nice to have

  • 10+ years of experience in software engineering and solution architecture
  • 10+ years of experience in enterprise architecture and design patterns
  • 10+ years of experience in distributed HPC and ML systems
  • 10+ years of experience in ML development lifecycle
  • 10+ years of experience in cloud computing
  • 10+ years of experience in data architecture
  • 10+ years of hands-on coding in Python, Java, Go, Scala, JavaScript/TypeScript, or similar
  • 8+ years owning the full SDLC, conception through architecture, implementation, testing, deployment, and production support
  • Experience working directly with quantitative analysts or data scientists — model development lifecycles, feature pipelines, backtesting, ML deployment, and the rigor to validate what a model is actually doing

What the JD emphasized

  • Designing and building distributed HPC and ML systems
  • Full ML development lifecycle using AI and ML frameworks
  • Production experience building LLM-backed or agentic systems — prompt engineering, evaluation frameworks, observability, and guardrails included

Other signals

  • AI-native engineering standards
  • liquidity management, predictive analytics and financial planning infrastructure
  • emerging AI frameworks and architectural patterns
  • AIOps practices
  • building LLM-backed or agentic systems