Senior Lead AI Engineer (LLM Gateway, FM Hosting)

Responsibilities

• Collaborate with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that transform how associates work and how customers engage 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.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.
• Invent and apply state-of-the-art LLM optimization techniques to improve scalability, cost, latency, and throughput for large-scale production AI systems.
• Contribute to the technical vision and long-term roadmap of foundational AI systems at Capital One.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or Master’s degree in those fields plus at least 4 years of experience.
• At least 6 years of experience programming with Python, Go, Scala, or Java.

Technologies

• AWS Ultraclusters
• Huggingface
• VectorDBs
• Nemo Guardrails
• PyTorch
• Python
• Go
• Scala
• Java

Benefits

• Health benefits
• Performance-based incentive compensation (cash bonuses and/or long-term incentives)

Overview

Capital One is focused on creating responsible and reliable AI systems to transform banking. The company has a history of using machine learning to deliver real-time, personalized experiences and invests in technology infrastructure and talent to stay at the forefront of enterprise AI. This role supports that mission by advancing foundational AI capabilities and scalable solutions for financial services.

Team Description

The Intelligent Foundations and Experiences (IFX) team sits at the core of capitalizing AI across Capital One. We partner with groups across the company to push the boundaries of AI engineering, building proprietary solutions central to the business and delivering value to millions of customers. Our AI models and platforms empower teams to integrate AI into products and services that matter in everyday banking.

The Ideal Candidate

• You enjoy building systems, take pride in code quality, and share a commitment to doing the right thing to impact banking for good.
• You stay current with AI research and can translate publications into production-ready techniques.
• You adapt quickly, bring clarity to undefined problems, ask critical questions, and communicate findings concisely; you are unafraid to propose new ideas, even if unproven.
• You are deeply technical, with a strong foundation in engineering and mathematics, capable of spotting optimization opportunities across hardware, software, and AI.
• You are a resilient trailblazer who can forge paths to meet business goals when the route is unclear.

Basic Qualifications

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or Master’s degree in those fields plus at least 4 years of experience.
• At least 6 years of experience programming with Python, Go, Scala, or Java.

Preferred Qualifications

• 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g., AWS, Google Cloud, Azure, or equivalent private cloud).
• Experience designing, developing, integrating, delivering, and supporting complex AI systems.
• Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders.
• Experience developing AI and ML algorithms or technologies (e.g. LLM inference, similarity search and VectorDBs, guardrails, memory) using Python, C++, C#, Java, or Golang.
• Experience developing and applying state-of-the-art techniques to optimize training and inference software for better hardware utilization, latency, throughput, and cost.
• Passion for staying abreast of the latest AI research and systems, with the ability to judiciously apply novel techniques in production.
• Excellent communication and presentation skills, capable of explaining complex AI concepts to peers.