AI Engineer 5

Capital One is seeking an AI Engineer 5 to build and deploy responsible, scalable AI systems that support modern banking experiences. This onsite role in McLean, VA focuses on designing AI software components, optimizing production performance, and leading technical governance through design reviews.

Role Overview

In this position, you will partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers. The work includes developing and operating foundation model and large language model (LLM) capabilities, evaluating system behavior, and ensuring production readiness through governance and observability.

Key Responsibilities

• Collaborate cross-functionally to deliver AI-powered products that change how associates work and how customers interact with Capital One.
• 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 a broad stack of open source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch.
• Identify and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput for 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 that integrate LLMs, vector search, and domain-specific models into unified systems.
• 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 maintain technical consistency and compliance with AI engineering standards.
• Mentor Principal and Manager-level AI engineers, supporting cross-domain learning and improving organizational technical maturity.

Required Qualifications

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

Technologies

• 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 (RAG), human-in-the-loop
• Foundation model training, agents and multi-agent workflows
• Multi-model orchestration pipelines, vector search

Team Context

The Intelligent Foundations and Experiences (IFX) team brings Capital One’s AI vision to life. The team works with partners across the company to advance the state of the art in science and AI engineering, building and deploying proprietary solutions central to the business and delivering value to millions of customers. AI models and platforms within the organization enable teams across Capital One to enhance products with responsible and scalable AI for high-leverage impact.

Ideal Candidate Profile

• Enjoys building systems with a focus on quality and a commitment to responsible work that supports meaningful change in banking.
• Stays current with research, understands scientific publications, and applies novel techniques in production.
• Works through large, undefined problems, asks questions to identify root causes, and communicates findings clearly.
• Has deep technical strength across engineering and mathematics, with experience spanning hardware, software, and AI to surface optimization opportunities.
• Demonstrates resilience and trailblazing skills by forging new paths to meet business goals when the route is not established.

Preferred Qualifications

• Experience leading development of AI systems with tradeoffs across cost, latency, throughput, and accuracy.
• 7+ years 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 (including LLM Inference, Similarity Search, and VectorDBs, Guardrails, and Memory) using Python, C++, C#, Java, CUDA, or Golang.
• Experience applying state-of-the-art techniques to optimize 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, including the ability to articulate complex AI concepts.
• 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, token inputs, and token outputs.

Location and Compensation

• Location: McLean, VA (onsite)
• Compensation: USD 229,900 - 262,400 per year