AI Engineer 5 (AI Foundations: LLM Customization, Finetuning, Reinforcement Learning)

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Capital One's Intelligent Foundations and Experiences team is hiring a senior AI Engineer to build and deploy production AI systems, including foundation model training, LLM inference, agentic workflows, and vector search. The role focuses on optimizing large-scale AI systems for scalability, cost, latency, and throughput while contributing to the technical roadmap for foundational AI at Capital One. You will lead design reviews, mentor senior engineers, and partner with cross-functional teams to deliver AI-powered banking products.

• Design, develop, test, deploy, and support AI software components including foundation model training, LLM inference, agents, similarity search, guardrails, and model evaluation
• Optimize foundation model techniques to improve scalability, cost, latency, and throughput of production AI systems
• Design and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models
• Lead cost-performance governance reviews tracking GPU utilization, model throughput, and inference cost efficiency
• Lead design councils and review boards to ensure technical consistency and AI engineering standards
• Mentor Principal and Manager-level AI engineers and elevate organizational technical maturity

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related field plus at least 6 years of experience developing AI/ML algorithms or technologies, or Master's degree plus at least 4 years
• At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java
• Experience with LLM inference, similarity search, vector databases, guardrails, and memory systems
• Experience deploying scalable AI solutions on cloud platforms such as AWS, Google Cloud, or Azure

• 7 years of experience deploying scalable and responsible AI solutions on cloud platforms
• Experience building agentic AI systems and agentic workflows
• Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
• Experience architecting heterogeneous AI systems including rule-based, retrieval-augmented, and generative components
• Experience defining standards for ethical AI deployment including explainability, fairness, and human-in-the-loop review
• Strong communication and presentation skills