AI Engineer 4 (Gen AI Platform Services: Agentic AI, Guardrails, Evaluation)

Capital One builds AI platforms that help teams across the company enhance products with reliable, responsible, and scalable model capabilities. On the Intelligent Foundations and Experiences (IFX) team, you will help bring the vision for AI to life by engineering GenAI platform services that support agentic AI, guardrails, and evaluation at production scale.

This AI Engineer 4 role is based in McLean, VA (onsite) and focuses on end-to-end architecture and delivery of foundation model services. You will help define reliability expectations through SLOs, ensure maintainability and ethical alignment, and support deployment, observability, and governance for large-scale AI systems.

What you’ll do

• Work 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 spanning foundation model training, large language model 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 tools, including AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch.
• Introduce foundation model optimization techniques that improve performance in production, targeting scalability, cost, latency, and throughput.
• Own end-to-end architecture for complex AI systems, with a focus on maintainability, observability, and ethical alignment.
• Define and maintain service-level objectives (SLOs) for reliability, including latency, uptime, and model performance drift.
• Partner with infrastructure engineering to optimize GPU/TPU utilization and accelerate model inference pipelines.
• Lead cross-functional technical reviews for new AI system deployments, ensuring security, data governance, and compliance standards are met.
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation.

What you’ll need

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI/ML algorithms or technologies, or Master’s degree plus at least 2 years.
• At least 4 years programming with Python, Go, Scala, CUDA, or Java.
• Experience leading development of AI systems with tradeoff decisions across cost, latency, throughput, and accuracy.
• 6+ years 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 AI services.
• Experience developing AI/ML algorithms or technologies such as 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 for improved hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and agentic workflows.
• Proficiency designing distributed systems for model training, evaluation, and online inference at petabyte scale.
• Experience defining AI model governance processes, including producibility, lineage tracking, and automated retaining schedules.
• Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms.

Tools and technologies

AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, AWS, Google Cloud, Azure, Python, Go, Scala, CUDA, Java, C++, C#, Golang, GPU, TPU, foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, observability.

Benefits

• Health insurance
• Financial and other benefits

Salary and incentives

McLean, VA: USD 197,300 - 225,100 per year for AI Engineer 4.

This role may also be eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).