According to Senior Lead Engineer Alexander, working at Capital One is different from what people might expect. Rather than spend his days crunching numbers and analyzing market trends, Alexander stays busy “solving really cool puzzles with really smart people” — a role he considers a “tremendous privilege” to hold. He said his day-to-day work revolves around maintaining the tools and infrastructure tied to one of the company’s enterprise AI/ML platforms, which serves as a foundation for wider tech initiatives. “It lets our data scientists really hit the ground running and start delivering value at a much faster rate,” Alexander explained.
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Fintech • Machine Learning • Payments • Software • Financial Services
Develop and deploy scalable, responsible AI systems, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Partner with engineering, research, product, and program teams to deliver AI-powered products. Apply advanced optimization techniques to improve performance, scalability, latency, throughput, hardware utilization, and cost, while shaping the long-term roadmap for foundational AI platforms.
Fintech • Machine Learning • Payments • Software • Financial Services
Design, develop, test, deploy, and support scalable AI systems and software components, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Partner with engineering, research, program management, and product teams to deliver AI-powered products. Develop optimization techniques to improve performance, scalability, latency, throughput, hardware utilization, and cost while contributing to the long-term technical roadmap for foundational AI systems.
Fintech • Machine Learning • Payments • Software • Financial Services
Leads data science for Capital One’s Business Cards and Payments Credit Infrastructure team. Responsibilities include partnering with data scientists, engineers, and product managers; conducting statistical analysis; building, validating, and implementing machine learning models; analyzing large-scale numeric and textual data; translating technical findings into business outcomes; and developing team talent.
