AI Engineer

About The Company Capital One is a leading financial services organization committed to transforming banking through innovative technology and responsible AI systems. With a longstanding reputation for leveraging machine learning and data-driven insights, Capital One has established itself as an industry pioneer in delivering personalized, real-time customer experiences. The company invests heavily in cutting-edge technology infrastructure and attracts world-class talent to stay at the forefront of AI advancements. Its mission is to reimagine banking by integrating emerging AI capabilities that enhance customer engagement, streamline operations, and promote responsible financial practices. Capital One’s dedication to responsible AI development underscores its commitment to ethical standards, regulatory compliance, and fairness in all its AI applications.

About The Role The Director of AI Engineering at Capital One will play a pivotal role in shaping the company’s AI strategy and leading the development of scalable, responsible AI systems. This remote-eligible leadership position involves overseeing a team of engineers, research scientists, and product managers dedicated to building innovative AI solutions that transform how the bank serves its customers and supports its business objectives. The successful candidate will be responsible for managing the end-to-end lifecycle of AI software components, including foundation model training, large language model inference, similarity search, and governance frameworks. They will make strategic build-vs-buy decisions, optimize AI models for performance and cost-efficiency, and contribute to the long-term technical vision of Capital One’s AI ecosystem. This role offers an exciting opportunity to lead a high-impact team, foster innovation, and ensure the responsible deployment of AI technologies aligned with enterprise standards and regulatory requirements.

Qualifications The ideal candidate will possess a strong technical background complemented by proven leadership experience. A minimum of a Bachelor’s Degree in Computer Science, AI, Electrical Engineering, or related fields, along with at least 8 years of experience developing AI and ML algorithms, is required. Alternatively, a Master’s Degree with at least 6 years of relevant experience will also be considered. Candidates should have at least 3 years of experience leading engineering teams, demonstrating the ability to manage complex projects and mentor talent. Preferred qualifications include over 5 years of managing AI engineering teams, extensive experience deploying scalable AI solutions on cloud platforms such as AWS, Google Cloud, or Azure, and proficiency in programming languages like Python, C++, or Java. Deep understanding of AI research, responsible AI practices, and experience in building agentic AI systems are highly desirable. Excellent communication skills and a strategic mindset are essential to articulate complex concepts and drive enterprise-wide AI initiatives.

Responsibilities

• Partner with cross-functional teams, including engineers, research scientists, product managers, and program managers, to deliver AI-powered products that enhance customer experience and operational efficiency.
• Oversee the design, development, testing, deployment, and ongoing support of AI software components such as foundation models, large language models, similarity search algorithms, and governance frameworks.
• Make informed build-vs-buy decisions across a broad spectrum of AI technologies, leveraging open-source tools and SaaS platforms like Huggingface, AWS Ultraclusters, and VectorDBs.
• Innovate by introducing state-of-the-art foundation model optimization techniques to improve performance metrics including scalability, latency, throughput, and cost-efficiency.
• Contribute to the strategic vision and roadmap for AI systems, ensuring alignment with enterprise goals and regulatory standards.
• Attract, develop, and retain top AI talent, fostering a culture of continuous learning, innovation, and ethical AI practices.
• Translate enterprise AI strategies into actionable, portfolio-level execution plans, balancing innovation with reliable delivery.
• Scale AI engineering practices through shared infrastructure, reusable components, and unified observability and governance frameworks.
• Establish enterprise standards for Responsible AI, including fairness metrics, evaluation protocols, …