AI Engineer 4 (Gen AI Platform Services)

Capital One • San Jose, CA

3d • $215,200 - $245,600 • Hybrid

About The Position

Capital One is hiring an AI Engineer 4 for its Intelligent Foundations and Experiences (IFX) team, which develops proprietary AI models and platforms used across the company. The role partners with engineering, research, technical program, and product teams to build AI-powered products and scalable foundational AI systems for associates and customers.

The engineer designs, develops, tests, deploys, and supports AI components and systems, including foundation-model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The role uses open-source and SaaS AI technologies and develops optimization techniques to improve production systems’ scalability, cost, latency, and throughput. It contributes to technical vision and roadmap, owns architecture for complex AI systems, defines reliability objectives, works with infrastructure teams on GPU/TPU utilization and inference, and leads deployment reviews for security, data governance, and compliance. The role also mentors senior technical colleagues.

This is a hybrid position, with on-site presence required according to Capital One’s hybrid policy. The role may be eligible for performance-based incentive compensation, including cash bonuses and/or long-term incentives. Capital One states that it will consider sponsoring a new qualified applicant for employment authorization.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, plus at least 4 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of these fields or a related field, plus at least 2 years of such experience.
• At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.

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 for hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Passion for following the latest AI research and systems and applying novel techniques judiciously in production.
• Proficiency 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 of new AI system deployments to ensure security, data governance, and compliance standards are met.
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

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