AI Engineer 4 (Gen AI Platform Services)

Capital One • San Francisco, CA

1d • Hybrid

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI solutions and platforms used across the company to deliver responsible, scalable value to customers and associates. The AI Engineer will partner with engineers, research scientists, technical program managers, and product managers to develop AI-powered products and foundational AI systems.

The role involves designing, developing, testing, deploying, and supporting AI components such as foundation-model training, large language model inference, agent and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The engineer will use open-source and SaaS technologies, develop optimization techniques for production AI systems, and contribute to the technical vision and long-term roadmap. Responsibilities also include owning architecture for complex AI systems, defining reliability objectives, improving accelerator utilization and inference pipelines, leading technical deployment reviews, and mentoring colleagues.

This is a hybrid position subject to Capital One’s hybrid policy and required on-site presence at the designated office location. The role is eligible for performance-based incentive compensation, which may include bonuses and/or long-term incentives. Capital One offers health, financial, and other benefits; eligibility varies by employment status and management level.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field and at least 4 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of these fields and at least 2 years of such experience.
• At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.
• Strong foundation in engineering and mathematics, with expertise in hardware, software, and AI.
• Ability to understand scientific publications and apply novel techniques in production.
• Ability to adapt quickly, bring clarity to undefined problems, investigate root causes, and communicate findings clearly.
• Passion for building high-quality systems and pursuing responsible approaches to AI.
• Must meet the stated experience requirement through one of the degree-and-experience combinations listed in the basic qualifications.

Nice To Haves

• Experience leading AI system development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• 6 or more years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
• Experience designing, developing, delivering, and supporting AI services.
• Experience developing AI and ML algorithms or technologies, such as LLM inference, similarity search and VectorDBs, guardrails, or memory, using Python, C++, C#, Java, CUDA, or Golang.
• Experience developing and applying state-of-the-art techniques to optimize training and inference software, hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Passion for staying current with AI research and systems and applying appropriate novel techniques in production.
• Proficiency in designing distributed systems for model training, evaluation, and online inference at petabyte scale.
• Experience defining AI model governance processes, including producibility, lineage tracking, and automated retraining schedules.
• Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms.

Responsibilities

• Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• Design, develop, test, deploy, and support AI software components, including foundation-model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
• Use open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch.
• Develop foundation-model optimization techniques to improve scalability, cost, latency, and throughput of production AI systems.
• Contribute to the technical vision and long-term roadmap for foundational AI systems.
• Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment.
• Define and maintain service-level objectives for AI reliability, including latency, uptime, and model-performance drift.
• Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model-inference pipelines.
• Lead cross-functional technical reviews for AI system deployments, ensuring security, data governance, and compliance standards are met.
• Mentor Principal and Senior Associates on scalable design, performance tuning, and translating research into production.
• Apply relevant AI research and novel techniques judiciously in production.
• Design, develop, deliver, and support AI services and systems, and make tradeoff decisions involving cost, latency, throughput, and accuracy.
• Define AI model governance processes, including producibility, lineage tracking, and automated retraining schedules.
• Influence architectural decisions across AI product lines or platforms.

Benefits

• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
• Comprehensive health, financial, and other benefits supporting employee well-being.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior