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

Capital One • San Jose, CA

9d • $215,200 - $245,600

About The Position

Capital One is hiring an AI Engineer 4 for its Gen AI Platform Services: Agentic Systems work. The role is part of the Intelligent Foundations and Experiences (IFX) team, which partners across the company to advance AI science and engineering and build proprietary solutions that support Capital One products and serve millions of customers. The team develops AI models and platforms to help deliver responsible, scalable AI capabilities.

The AI Engineer will collaborate with engineers, research scientists, technical program managers, and product managers to build AI-powered products. The work spans foundation-model training, large-language-model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The engineer will use open-source and SaaS AI technologies, develop optimization techniques for production AI systems, and help define the technical vision and roadmap for foundational AI systems.

The position owns end-to-end architecture for complex AI systems, with a focus on maintainability, observability, and ethical alignment. It includes defining AI reliability SLOs, improving GPU/TPU utilization and inference pipelines with infrastructure teams, leading technical reviews of deployments for security, governance, and compliance, and mentoring Principal and Senior Associates. This role is also eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

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 relevant master's degree plus at least 2 years of that 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+ 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 vector databases, guardrails, and 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 agentic workflows.
• Interest in 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, vector databases, and PyTorch.
• Develop foundation-model optimization techniques to improve production-system scalability, cost, latency, and throughput.
• 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 to ensure security, data-governance, and compliance standards are met.
• Mentor Principal and Senior Associates in scalable design, performance tuning, and research-to-production translation.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• 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