AI Engineer 5 (Gen AI SDK: Agentic AI, Guardrails, Evaluation)

Capital One • San Jose, CA

2d • $250,800 - $286,200

About The Position

Capital One is building responsible and reliable AI systems to improve banking and customer experiences. The Intelligent Foundations and Experiences (IFX) team works with partners across the company to advance AI science and engineering and build proprietary solutions and platforms that deliver scalable value to customers and internal teams.

The AI Engineer 5 partners with engineers, research scientists, technical program managers, and product managers to build AI-powered products. The role designs, develops, tests, deploys, and supports foundational AI software, including foundation-model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, evaluation, experimentation, governance, and observability. It applies open-source and SaaS AI technologies and develops optimization techniques to improve production systems’ scalability, cost, latency, and throughput.

The role also contributes to the technical vision and long-term roadmap for foundational AI systems. It designs and optimizes multi-model orchestration pipelines, leads cost-performance governance reviews and technical design councils, and mentors Principal- and Manager-level AI engineers.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field and at least 6 years of experience developing AI and ML algorithms or technologies; alternatively, a master’s degree in one of these fields and at least 4 years of that experience.
• At least 6 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.
• At least 7 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 complex AI systems.
• 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 training and inference optimization techniques to improve hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Interest in keeping up with AI research and systems and applying new techniques judiciously in production.
• Excellent communication and presentation skills, including explaining complex AI concepts to peers.
• Experience architecting and integrating rule-based, retrieval-augmented, and generative AI components into unified production pipelines.
• Experience defining and enforcing ethical AI deployment standards, including explainability, fairness, and human-in-the-loop review processes.
• Ability to balance model performance and operational cost through dynamic inference strategies and model compression.
• Experience right-sizing models, instance counts, and hardware types for requirements such as context length and input and output token volumes.

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 of Capital One’s foundational AI systems.
• Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models.
• Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency.
• Lead team design councils or design review boards to support technical consistency and compliance with AI engineering standards.
• Mentor Principal- and Manager-level AI engineers and foster cross-domain learning.

Benefits

• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
• A comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being.

What This Job Offers

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