Capital One is an industry leader in using machine learning to create personalized customer experiences and is seeking a Lead AI Engineer to help advance their AI vision. In this role, you will partner with cross-functional teams to design and develop AI-powered products, focusing on large language model inference and optimization techniques for production AI systems. Responsibilities: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One; Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc; Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more; Invent and introduce state-of-the-art LLM optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems; Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 2 years of experience developing AI and ML algorithms or technologies; At least 4 years of experience programming with Python, Go, Scala, or Java Required Skills: AI and ML algorithm development, Python programming, Go programming, Scala programming, Java programming, Cloud platforms (AWS, Google Cloud, Azure), AI service design and development, LLM inference, Similarity search, Vector databases, AI guardrails, AI model training optimization, AI inference optimization, Hardware utilization optimization, Latency optimization, Throughput optimization, Cost optimization