AI Engineer (Remote)

About The Company Capital One is a leading financial institution committed to creating responsible and reliable AI systems that transform banking for the better. With years of experience leveraging machine learning to deliver personalized, real-time customer experiences, Capital One continues to invest heavily in technology infrastructure and world-class talent. Our expertise in AI and ML enables us to build innovative applications that bring simplicity and humanity to banking, such as fraud detection and real-time customer support. We are dedicated to building top-tier applied science and engineering teams to deliver breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will have the opportunity to harness emerging AI capabilities to revolutionize how we serve our customers and businesses.

About The Role The Staff AI Engineer will join the Intelligent Foundations and Experiences (IFX) team, which plays a pivotal role in realizing Capital One’s AI vision. This team collaborates closely with cross-functional partners to advance AI science and engineering, developing proprietary AI solutions that impact millions of customers. The role involves designing, developing, deploying, and supporting advanced AI software components including foundation model training, large language model inference, multi-agent workflows, and more. You will work with cutting-edge open source and SaaS AI technologies, invent optimization techniques for large-scale AI systems, and contribute to the technical vision and architecture of enterprise-wide AI platforms. This position requires a passion for responsible AI principles, mentorship, and driving internal education on best practices. The Staff AI Engineer role is remote eligible and offers the chance to influence the future of AI at Capital One.

Qualifications

• Bachelor’s Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master’s degree plus at least 6 years of relevant experience
• At least 8 years of programming experience in Python, Go, Scala, CUDA, or Java
• Experience designing AI systems balancing cost, latency, throughput, and accuracy
• 8 years of experience deploying scalable AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private clouds
• Proven expertise in architecting, developing, integrating, and supporting complex AI systems
• Leadership experience mentoring engineering teams and influencing cross-functional stakeholders
• Experience with AI and ML technologies including LLM inference, similarity search, vector databases, guardrails, and memory systems
• Proficiency in optimizing training and inference software for hardware utilization, latency, throughput, and cost
• Experience building agentic AI systems and workflows
• Strong communication skills to articulate complex AI concepts
• Track record of defining enterprise AI architecture standards, including data pipeline governance and observability frameworks
• Experience leading federated or multi-cloud AI strategies
• Ability to influence research-to-production processes and define metrics balancing business value, innovation, cost, and ethical responsibility
• Experience with model right-sizing and hardware selection based on requirements

Responsibilities

• Collaborate with engineers, research scientists, program managers, and product managers to deliver AI-powered products that improve customer and associate experiences
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agent workflows, similarity search, guardrails, model evaluation, and observability
• Utilize a broad stack of open source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch
• Invent and implement foundation model optimization techniques to enhance scalability, cost-efficiency, latency, and throughput of AI systems
• Contribute to the technical vision and long-term roadmap for foundational AI systems
• Set technical direction for enterprise-wide AI architecture, unifying tooling, observability, and deployment standards
• Design and integrate model routing, caching, and orchestration systems supporting hybrid and multi-model workloads
• Champion responsible AI principles, embedding transpare…