Senior 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.
• Develop trace capture, observability, and debugging capabilities for AI-assisted engineering workflows.
• Integrate evaluation results into CI/CD and release workflows, including quality gates for security and performance.
• Build reusable APIs, services, developer tooling, and dashboards to enable consistent AI-native engineering practices.
• Collaborate with Cyber, Risk, and Responsible AI teams to ensure solutions meet enterprise standards for auditability and compliance.
• Write architecture proposals, design documents, and technical guidance to scale engineering patterns across the company.

Requirements

• Bachelor’s Degree in a relevant field.
• At least 6 years of experience in software engineering.
• At least 1 year of experience with cloud computing (AWS, Azure, or Google Cloud).
• Expertise in full-stack development and distributed microservices.

Preferred Qualifications

• Master’s Degree in a technical field.
• 9+ years of experience in JavaScript, Java, TypeScript, SQL, Python, or Go.
• 4+ years of experience with cloud-native architectures, containers, Kubernetes, or serverless platforms.
• 2+ years of experience with CI/CD, automated testing, or observability frameworks like OpenTelemetry.
• 1+ years of experience integrating LLM APIs or building workflows around generative models.
• 1+ years of people management experience.

Benefits

• Comprehensive health, financial, and wellness benefits.
• Performance-based incentive compensation, including cash bonuses or long-term incentives.
• Inclusive culture focused on diversity, equity, and belonging.

About the Company

Capital One is a technology-driven financial institution that builds and operates critical enterprise platforms. Our AI Harness Engineering team focuses on enabling governed AI-native software delivery by building the tools, contracts, and evaluation systems that allow engineering teams to use AI models with enterprise-grade quality and operational discipline.