Jobs at Capital One

Leads engineering teams building responsible, scalable AI systems and products. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, observability, and production deployment. Makes build-versus-buy decisions across AI technologies, develops LLM optimization techniques, shapes foundational AI roadmaps, and recruits and mentors engineering talent. Partners with research, engineering, product, and program teams to deliver high-performance AI capabilities.

Design, develop, deploy, and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to long-term platform strategy, apply advanced AI research in production, and collaborate with engineers, scientists, program managers, and product leaders while mentoring teams.

Lead the design, development, deployment, and support of foundational AI systems, including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques that improve scalability, cost, latency, throughput, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to AI platform strategy, technical vision, and long-term roadmaps.

Lead a team of software developers and oversee diverse technology projects involving distributed microservices and full-stack systems. Build cloud-based solutions using multiple programming languages, databases, Docker, Kubernetes, and AWS services. Mentor engineers, collaborate with product managers, adopt emerging technologies, and support regulatory and customer-focused business needs.

Lead the design, development, testing, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Collaborate with engineering, research, program management, and product teams while shaping the technical vision and roadmap for responsible AI platforms.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Establish technical vision and roadmaps, apply emerging research in production, and lead engineering teams while influencing cross-functional stakeholders.

In this role, you will lead AI projects, collaborating with cross-functional teams to develop and deploy advanced AI solutions, focusing on LLM inference and model optimization.

Lead a 10-person user research team to build cumulative user intelligence for customer and developer platforms. Develop scalable research programs, enable continuous discovery and rapid testing using human-centered and AI methods, drive experience measurement, collaborate with senior leaders across design, product, and engineering, and recruit, develop, and manage top research talent to inform product decisions and business strategy.

This role involves partnering with teams to develop AI-powered technology, advancing applied research in AI, and delivering impactful models. Candidates will work on foundational models and leverage new technologies to optimize customer interactions with banking services.

The Senior Lead AI Engineer will develop and support AI software components, partner with cross-functional teams, and enhance AI systems performance. Responsibilities include designing, deploying, and optimizing AI models for improved banking services.

Leads product strategy and execution for integrating acquired companies and partnerships into Capital One’s Finance ecosystem. Establishes integration frameworks, aligns cross-functional workstreams, manages risks and blockers, and communicates strategies to executives. Uses data to assess opportunities and delivery performance while driving technology initiatives and organizational change. Also mentors product managers, shapes team culture, and promotes product management practices across Finance.

Lead design, build, deploy, and support GenAI platform services (foundation model training, LLM inference, similarity search, guardrails, evaluation, observability). Optimize LLM performance and scalability, collaborate cross-functionally, contribute to technical vision and roadmap.

Lead engineering teams to design, build, and scale back-end microservices and ML platform infrastructure. Collaborate with product managers and data scientists to deploy cloud-based solutions, mentor engineers, evaluate code, and ensure regulatory and operational needs are met using modern cloud, container, database, and ML tooling.

Lead and grow multiple product-focused engineering teams to design, build, deploy, and operate scalable, highly available full-stack and microservice solutions on AWS. Mentor engineers, drive engineering practices, break monoliths into microservices, improve velocity, and collaborate with product managers to deliver finance-focused systems including event-driven streaming and integrations.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.

Lead modernization of enterprise AI/ML infrastructure and analyst workflows. Build agentic, scalable, secure platform services, integrate generative AI and low-code/no-code tooling, scale AutoML for tabular and time-series, and engineer automated governance and controls while mentoring teams and influencing cross-functional stakeholders.

As an Applied Researcher II, you will create AI systems for banking, working with cross-functional teams to develop and implement AI-powered products, while conducting applied research and engaging with emerging technologies.

Lead a portfolio of technology projects with a team of developers to create solutions for regulatory needs, collaborating on cloud-based projects while mentoring engineers.

Lead a team building cloud-based full stack solutions and distributed microservices to meet regulatory and customer needs. Mentor engineers, collaborate with product managers, experiment with emerging tech, and deliver scalable AWS containerized systems using multiple languages and databases.

As an Applied Researcher I, you'll work with cross-functional teams to develop AI-powered products, engage in applied research, and build foundation models using various technologies.