AI Engineer 5

Capital One • San Jose, CA

4d

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team builds proprietary AI models and platforms that enable teams across the company to improve products and customer experiences responsibly and at scale. The AI Engineer will partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.

The role involves designing, developing, testing, deploying, and supporting 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. The engineer will use open-source and SaaS technologies, develop optimization techniques for large-scale production AI systems, and contribute to the technical vision and long-term roadmap for foundational AI systems.

The position also focuses on integrating LLMs, vector search, and domain-specific models into multi-model orchestration pipelines; leading cost-performance governance reviews; and guiding technical consistency through design councils and review boards. The role includes mentoring Principal- and Manager-level AI engineers and promoting cross-domain learning and organizational technical maturity.

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; or a master's degree in one of these fields and 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 systems development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• 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, VectorDBs, 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 current AI research and systems, and ability to apply 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.
• 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, token inputs, and token outputs.

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, VectorDBs, and PyTorch.
• Develop and introduce 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 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, fostering cross-domain learning and organizational technical maturity.

Benefits

• Comprehensive 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