AI Engineer 4 (Gen AI Platform Services: Agentic AI)

Capital One • San Francisco, NY

1d

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI solutions and platforms that enable teams across the company to deliver responsible, scalable AI products. The AI Engineer 4 partners with engineers, research scientists, technical program managers, and product managers to develop AI-powered products for associates and customers.

The role designs, develops, tests, deploys, and supports AI software components, including foundation-model training, large-language-model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It uses open-source and SaaS AI technologies and develops optimization techniques for the scalability, cost, latency, and throughput of production AI systems. The engineer contributes to the technical vision and roadmap for foundational AI systems and owns end-to-end architecture for complex AI systems, with attention to maintainability, observability, reliability, and ethical alignment.

The role defines and maintains AI reliability service-level objectives, works with infrastructure engineering to optimize GPU/TPU utilization and inference pipelines, leads cross-functional technical reviews for new deployments, and mentors Principal and Senior Associates. Capital One describes the ideal candidate as technically strong, curious, resilient, quality-focused, able to navigate ambiguity, and interested in applying current AI research responsibly in production.

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 those 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 judiciously apply novel techniques in production.
• Ability to adapt quickly, clarify ambiguous problems, investigate root causes, and communicate findings clearly.

Nice To Haves

• Experience leading AI systems development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• 6+ years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud.
• 3+ years of experience with Kubernetes.
• Experience designing, developing, delivering, and supporting AI services.
• Experience developing AI and ML algorithms or technologies, such as LLM inference, similarity search and vector databases, guardrails, or memory, using Python, C++, C#, Java, CUDA, or Golang.
• Experience developing and applying state-of-the-art training and inference optimization techniques to improve hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Passion for keeping current with AI research and systems and applying 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, vector databases, and PyTorch.
• Develop foundation-model optimization techniques to improve the scalability, cost, latency, and throughput of production AI systems.
• Contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One.
• 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 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