Lead Software Engineer, Full Stack

Responsibilities

• Lead the design and implementation of AI-native software engineering harnesses using approved models and coding agents.
• Build agentic workflow orchestration systems supporting planning, code generation, validation, and human checkpoints.
• Define typed input/output contracts, schemas, and workflow state models to ensure auditable and repeatable execution.
• Develop observability and debugging capabilities for AI-assisted workflows, including prompt lineage and tool-call traces.
• Integrate evaluation results into CI/CD pipelines, including quality gates for security, performance, and build correctness.
• Build reusable APIs, developer tooling, and dashboards to enable consistent adoption of AI-native engineering practices.
• Partner with application teams to migrate software using governed agentic workflows while maintaining enterprise controls.
• Collaborate with Cyber, Risk, and Architecture teams to ensure solutions meet security and compliance standards.

Requirements

• Bachelor's Degree.
• At least 4 years of experience in software engineering (internship experience does not apply).
• At least 1 year of experience with cloud computing (AWS, Microsoft Azure, or Google Cloud).

Preferred Qualifications

• Master's Degree.
• 7+ years of experience in JavaScript, Java, TypeScript, SQL, Python, or Go.
• 4+ years of experience utilizing open-source frameworks to build production-grade applications.
• 3+ years of experience with cloud-native architectures, microservices, Kubernetes, or serverless platforms.
• 2+ years of experience with CI/CD, automated testing, or observability frameworks (e.g., OpenTelemetry).
• 1+ years of experience integrating LLM APIs or building application workflows around generative models.
• Experience working within a regulated enterprise environment alongside cybersecurity and risk stakeholders.

About the Company

Capital One's Enterprise Platforms Technology team builds and operates critical enterprise platforms. The AI Harness Engineering team specifically focuses on enabling governed AI-native software delivery by building the tools, contracts, and evaluation systems that allow engineering teams to use approved AI models and coding agents with enterprise-grade quality and operational discipline.