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As a Lead AI Engineer, you will design, develop, and support AI software components while partnering with cross-functional teams to deliver AI-powered products at Capital One.

The Senior Lead AI Engineer will develop and support AI components, lead a cross-functional team, and drive innovation in AI systems at Capital One.

The role involves building machine learning models, collaborating with teams, and leveraging technology to analyze data, ultimately enhancing credit underwriting for business cards.

The Applied Researcher II will develop AI-powered products, partner with cross-functional teams, engage in applied research, and implement AI foundation models.

Lead the Design of Experiments center of excellence, standardizing incrementality measurement, building testing infrastructure, and mentoring Data Scientists while optimizing customer experience and validating product initiatives.

Provide high-level administrative support to 4–6 executives: manage complex calendars, coordinate travel and expenses, organize meetings and events, prepare business communications, maintain cross-team relationships, perform general office tasks, and drive team activities to ensure smooth, efficient operations in a fast-paced environment.

Lead and deliver ML solutions for bank operations, partnering with cross-functional teams to design, train, validate, and deploy large-scale models (NLP, forecasting, anomaly detection) using cloud and open-source tools, and mentor technical talent.

The Product Design Manager will lead design efforts, ensure customer-centered solutions, and collaborate across teams to innovate product experiences while managing design processes and projects effectively.

Manage SDLC governance, controls compliance, and process improvements. Develop and document processes, privacy/security controls, metrics, and risk management. Partner with Product, Technology, Cyber, and Risk teams to identify data risks, drive automation, and report process health to leadership.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.