Senior Lead Software Engineer, Full Stack

Capital One Capital One · Banking · New York, NY +2

Lead the design and implementation of AI-native software engineering harnesses that use approved models, coding agents, and developer workflows in governed control-plane capabilities. Build agentic workflow orchestration systems that support planning, code generation, validation, retry loops, human checkpoints, and controlled promotion through delivery environments. Define and implement typed input/output contracts, schemas, metadata capture, and workflow state models that make agentic execution auditable and repeatable. Develop trace capture, observability, and debugging capabilities for AI-assisted engineering workflows, including prompt/output lineage, tool-call traces, model/runtime metadata, and failure analysis. Integrate evaluation results into CI/CD and release workflows, including quality gates for fidelity, build correctness, accessibility, security, performance, and human-review outcomes.

What you'd actually do

  1. Lead the design and implementation of AI-native software engineering harnesses that use approved models, coding agents, and developer workflows in governed control-plane capabilities
  2. Build agentic workflow orchestration systems that support planning, code generation, validation, retry loops, human checkpoints, and controlled promotion through delivery environments
  3. Define and implement typed input/output contracts, schemas, metadata capture, and workflow state models that make agentic execution auditable and repeatable
  4. Develop trace capture, observability, and debugging capabilities for AI-assisted engineering workflows, including prompt/output lineage, tool-call traces, model/runtime metadata, and failure analysis
  5. Integrate evaluation results into CI/CD and release workflows, including quality gates for fidelity, build correctness, accessibility, security, performance, and human-review outcomes

Skills

Required

  • Bachelor's Degree
  • 6 years of experience in software engineering
  • 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud)

Nice to have

  • Master's Degree
  • 9+ years of experience in at least one of the following: JavaScript, Java, TypeScript, SQL, Python, or Go
  • 4+ years of experience utilizing open-source frameworks to build production-grade applications.
  • 4+ years of experience with cloud-native architectures, microservices, APIs, containers, Kubernetes, serverless platforms, or event-driven systems using AWS, GCP, or Microsoft Azure.
  • 2+ years of experience with CI/CD, automated testing, quality gates, developer productivity platforms, or observability frameworks (such as OpenTelemetry for logging, metrics, tracing, and dashboards).
  • 2+ years of experience working within an Agile delivery environment utilizing Agile practices and frameworks.
  • 1+ years of experience integrating LLM APIs, building application workflows around generative models, or deploying AI-assisted developer tools into production or enterprise software workflows.
  • 1+ years of experience building or operating software evaluation frameworks, automated testing runners, regression test suites, or telemetry pipelines, schema-driven development, or workflow orchestration.
  • 1+ years of people management experience
  • Experience collaborating with cybersecurity, risk, compliance, or data governance stakeholders to deliver software within a regulated enterprise environment.
  • Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

What the JD emphasized

  • governed AI-native software delivery
  • approved AI models
  • coding agents
  • enterprise-grade quality
  • traceability
  • operational discipline
  • governed control-plane capabilities
  • agentic workflow orchestration systems
  • auditable and repeatable
  • AI-assisted engineering workflows
  • CI/CD and release workflows
  • governed agentic workflows
  • enterprise controls
  • security, auditability, compliance, and operational readiness
  • regulated enterprise environment

Other signals

  • AI-native software engineering harnesses
  • agentic workflow orchestration systems
  • developer workflows
  • evaluation systems
  • release controls