Jobs at Capital One

Leads enterprise AI/ML technical programs from strategy through production, including platform roadmaps, modernization, scalability, governance, executive communications, investment prioritization, and risk management. Establishes TPM practices and performance models, aligns cross-functional stakeholders, and mentors TPM leaders. The role requires extensive technical program management experience, distributed systems and cloud expertise, AI/ML lifecycle experience, and leadership in regulated environments.

Build, deploy, scale, and operate production machine learning models and platforms. Responsibilities include developing ML components, distributed data pipelines, cloud and Kubernetes infrastructure, model monitoring and retraining, CI/CD automation, responsible AI governance, and collaboration with product, data science, and engineering teams.

Leads strategic analysis and product innovation for Capital One’s Consumer and Developer Experience organization. Responsibilities include answering complex strategic questions, leading AI transformation initiatives, conducting analytics and market assessments, setting long-term product vision, developing prototypes with engineering, building stakeholder relationships, and advancing organizational capabilities through training and AI adoption.

Develop and lead Workday Finance integrations and configurations across General Ledger, Fixed Assets, Expenses, Consolidation, Invoices, Payments, and Suppliers. Translate business priorities into resilient solutions, automate workflows, improve processes, conduct testing and code reviews, and collaborate with product owners and finance stakeholders using Agile methodologies. Lead integrators and configurators while maintaining platform performance, quality, and enterprise standards.

Conduct applied AI research and build foundation models, including vision-language models, across design, training, evaluation, validation, and deployment. Collaborate with data scientists, engineers, and product managers to deliver scalable AI products. Develop research methods, reproduce and extend published work, optimize training and inference, and contribute to technical reports and conference submissions. Own research problems and agendas while translating complex findings into business outcomes.

Leads large-scale full-stack engineering initiatives, technical architecture, and developer teams building distributed, cloud-native systems. Designs reliable microservices and platform solutions using modern programming languages, databases, containers, and cloud services. Partners with product leaders and architects on technology roadmaps, mentors engineers, establishes engineering standards, drives observability and performance improvements, and leads cross-functional technical initiatives.

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

Build and deploy scalable, responsible AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architectures, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to platform roadmaps, and mentor engineering staff.

Leads project management and design operations for Capital One’s enterprise design organization. Develops operational strategy, manages cross-functional teams, oversees design standards and practice management, and drives complex portfolios and programs. Partners with executive, product, technology, and design leaders to prioritize resources, improve processes, establish metrics, and scale human-centered design practices. Serves as a strategic advisor while advancing organizational goals, operational excellence, and business outcomes.

Design, build, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, and mentor engineering teams.

Leads product strategy and operations for AI/ML platforms, enabling associates to build, train, deploy, and scale models and generative AI applications. Defines roadmaps, prioritization, operational frameworks, and strategic planning processes while advocating for data scientists and engineers. Manages teams, partners with Product, Engineering, TPM, and business functions, oversees concurrent programs, and applies analytics to product, marketing, and credit risk decisions.

Leads enterprise AI engineering strategy and multi-team execution. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and production optimization. Makes technology decisions, scales reusable AI infrastructure and responsible AI practices, partners with research and risk teams, and develops engineering talent. Requires strong hands-on expertise in AI systems, cloud deployment, model optimization, and organizational leadership.

Conduct applied AI research and build foundation models from design through production deployment. Partner with data scientists, engineers, and product managers to develop scalable models, optimize training and inference, evaluate emerging methods, and translate research into customer-facing banking products. Lead research initiatives, define research agendas, mentor colleagues, and deliver production-scale solutions involving language, graph, image, or sequential data.

Leads the enterprise vision and strategy for Capital One’s developer experience and Context Ecosystem. Oversees data products and measurement frameworks that assess software development effectiveness, translating developer experience metrics into improvements in engineering speed, quality, and efficiency. Builds product strategy, leads product management teams, drives cross-functional alignment, and manages large-scale enterprise platforms.

Build and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for latency, cost, throughput, scalability, and hardware utilization. Own complex architectures, reliability SLOs, security and compliance reviews, technical roadmaps, and cross-functional delivery while mentoring engineering teams.

Designs, builds, deploys, and maintains production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI, governance, and security practices. The role collaborates with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.

Leads project and program management across feasibility, planning, prioritization, implementation, and closeout. Establishes structure, methodologies, documentation, metrics, and transparency in complex environments. Partners with cross-functional stakeholders, influences delivery, manages competing priorities, and develops high-performing teams. Requires extensive project or process management and people leadership experience, with responsibility for strategic and tactical execution.

Build and lead full-stack software engineering initiatives using distributed microservices, cloud platforms, APIs, databases, and modern front-end technologies. Provide technical leadership across Agile teams, establish engineering standards, mentor developers, partner with architects and product managers, reduce technical debt, improve system performance, and influence long-term technology strategy. The role emphasizes AWS and other cloud technologies, open-source frameworks, CI/CD, automated testing, observability, and AI-assisted development tools.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and hardware utilization. Own complex architectures, define reliability objectives, lead technical reviews, ensure security and compliance, shape long-term AI roadmaps, and mentor engineering staff.