Capital One is hiring an AI Engineer to help build responsible, scalable foundation AI and LLM capabilities for banking.

Collaborate with a cross-functional group of 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.
Apply open source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
Develop state-of-the-art foundation model optimization techniques to improve scalability, cost, latency, and throughput in production AI systems.
Help shape the technical vision and long-term roadmap for foundational AI systems at Capital One.
Design, implement, and optimize multi-model orchestration pipelines that integrate LLMs, vector search, and domain-specific models into unified systems.
Establish and lead cost-performance governance reviews across AI systems, including 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 to support cross-domain learning and raise organizational technical maturity.

Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field plus at least 6 years of experience developing AI/ML algorithms or technologies, or a Master’s degree plus at least 4 years of experience developing AI/ML algorithms or technologies.
At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.

Python, Go, Scala, CUDA, Java
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, AWS, Google Cloud, Azure
C++, C#, Golang
LLM Inference, Similarity Search, Guardrails, Memory, retrieval-augmented
Foundation model training, agents and multi-agent workflows, multi-model orchestration pipelines, vector search

The Intelligent Foundations and Experiences (IFX) team is central to bringing Capital One’s AI vision to life.
Partners with teams across the company to advance science and AI engineering, and builds and deploys proprietary solutions used across business and delivered to millions of customers.
Enables teams to enhance products with responsible and scalable AI for high-leverage impact.

Enjoys building systems, takes pride in quality, and focuses on doing the right thing.
Stays current with the latest research and can apply new techniques carefully in production.
Works through big, undefined problems by finding root causes and communicating findings clearly.
Has strong engineering and mathematics fundamentals, with expertise across hardware, software, and AI to uncover optimization opportunities.
Can pursue business goals even when the path is not fully known.

Experience leading development of AI systems with tradeoffs across cost, latency, throughput, and accuracy.
7+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g., 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 (e.g., LLM inference, similarity search and VectorDBs, guardrails, memory) using Python, C++, C#, Java, CUDA, or Golang.
Experience optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
Experience building agentic AI systems and agentic workflows.
Excellent communication and presentation skills for articulating 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 based on requirements such as context length and token inputs/outputs.

Location: McLean, VA (onsite)

Salary: USD 229,900 - 262,400 per year

Experience: 6+ years (minimum)

AI Engineer 5

Job Description