AI Engineer 5 (AI Foundations: Evaluations and Agentic Workflows)

Capital One • McLean, VA

1d • Hybrid

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI solutions and platforms that help teams across the company deliver responsible, scalable AI experiences. The AI Engineer 5 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 and systems, including foundation-model training, large-language-model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also develops and optimizes multi-model orchestration pipelines that integrate LLMs, vector search, and domain-specific models, and applies foundation-model optimization techniques to improve production-system scalability, cost, latency, and throughput.

The engineer contributes to the technical vision and long-term roadmap for foundational AI systems, establishes cost-performance governance reviews, and leads design councils or review boards to promote technical consistency and compliance with AI engineering standards. The role also mentors Principal- and Manager-level AI engineers and supports cross-domain learning and organizational technical maturity.

This is a hybrid position, subject to Capital One’s policy requiring on-site presence at the designated office location. The role is eligible for performance-based incentive compensation. 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 6 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of these fields, plus at least 4 years of relevant experience.
• At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.
• Strong engineering and mathematics foundation, with expertise in hardware, software, and AI.
• Ability to understand scientific publications and apply appropriate novel techniques in production.
• Ability to adapt quickly, bring clarity to undefined problems, investigate root causes, and communicate findings clearly.
• Resilience and ability to find new paths to achieve business goals.
• Hybrid work arrangement requiring on-site presence according to Capital One policy.

Nice To Haves

• Experience leading AI system development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• 7 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 complex AI systems.
• Experience developing AI and ML algorithms or technologies, such as LLM inference, similarity search, 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 up with AI research and applying novel techniques in production.
• Excellent communication and presentation skills, including the ability to explain complex AI concepts to peers.
• Experience architecting and integrating heterogeneous AI systems, including rule-based, retrieval-augmented, and generative components, into unified production pipelines.
• Experience defining and enforcing ethical AI deployment standards, including explainability, fairness, and human-in-the-loop review processes.
• Demonstrated ability to balance model performance and operational cost using dynamic inference strategies and model compression.
• Experience right-sizing models, instance counts, and hardware types based on requirements such as context length and input and output tokens.

Responsibilities

• Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.
• 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.
• Invent and introduce foundation-model optimization techniques to improve production AI system scalability, cost, latency, and throughput.
• Contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One.
• Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models.
• Establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency.
• Lead team design councils or design review boards to promote technical consistency and compliance with AI engineering standards.
• Mentor Principal- and Manager-level AI engineers, fostering cross-domain learning and raising organizational technical maturity.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Eligibility for 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