AI Engineer 5

Capital One • San Jose, CA

4d

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI solutions and platforms that support teams across the company and deliver value to millions of customers. The team works with partners across Capital One to advance AI science and engineering and to bring responsible, scalable AI capabilities into products and business operations.

The AI Engineer 5 partners with engineers, research scientists, technical program managers, and product managers to develop AI-powered products. The role designs, develops, tests, deploys, and supports AI software components, including foundation model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also advances production AI systems through optimization, orchestration, and cost-performance governance, and contributes to the technical vision and long-term roadmap for foundational AI systems.

The position involves working with open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, vector databases, and PyTorch. It also leads design reviews and governance efforts, mentors senior AI engineers, and helps ensure technical consistency and compliance with AI engineering standards. The role is suited to a deeply technical, research-oriented engineer who can navigate ambiguous problems and develop production solutions responsibly.

The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives. Capital One offers a comprehensive health, financial, and other benefits package; eligibility varies by employment status and management level.

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 such 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.
• 7 or more years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or an 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, 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 following AI research and systems developments and applying novel techniques judiciously 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 to requirements such as context length and token inputs and outputs.

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 large-scale production AI systems.
• 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 ensure technical consistency and compliance with AI engineering standards.
• Mentor Principal- and Manager-level AI engineers and foster cross-domain learning and organizational technical maturity.

Benefits

• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
• Comprehensive health, financial, and other benefits supporting total well-being.

What This Job Offers

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