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

Capital One is hiring an AI Engineer 4 (Gen AI Platform Services: Agentic AI, Guardrails, Evaluation) to help build and deploy responsible, scalable generative AI platform capabilities. In the Intelligent Foundations and Experiences (IFX) team, you will partner with engineers, research scientists, technical program managers, and product managers to advance AI engineering across the company and deliver proprietary solutions used by teams serving millions of customers.

This role centers on end-to-end delivery for modern AI systems, from architecture and model optimization to reliability metrics and production-grade observability. You will contribute to platform capabilities that help associates work differently and help customers interact with Capital One in smarter ways, with guardrails, evaluation, and governance built into the system.

What you’ll do

• Work with cross-functional partners to deliver AI-powered products across associate workflows and customer experiences.
• Design, develop, test, deploy, and support 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.
• Build using modern AI infrastructure and a blend of Open Source and SaaS technologies, including AWS Ultraclusters, Hugging Face, and VectorDBs, with frameworks such as PyTorch.
• Introduce state-of-the-art foundation model optimization techniques to improve scalability, cost, latency, and throughput for large-scale production AI systems.
• Own end-to-end architecture for complex AI systems, emphasizing maintainability, observability, and ethical alignment.
• Define and maintain service-level objectives (SLOs) for AI reliability, including latency, uptime, and model performance drift.
• Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model inference pipelines.
• Lead cross-functional technical reviews for new AI system deployments, supporting security, data governance, and compliance standards.
• Mentor Principal and Senior Associates on scalable design, performance tuning, and translating research into production.

What you bring

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years developing AI/ML algorithms or technologies, or a Master’s degree plus at least 2 years.
• 4+ years programming with Python, Go, Scala, CUDA, or Java.
• Experience leading AI system development with tradeoffs across cost, latency, throughput, and accuracy.
• 6+ 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 AI services.
• Experience building AI/ML solutions (for example: LLM inference, similarity search and VectorDBs, guardrails, memory) using Python, C++, C#, Java, CUDA, or Golang.
• Experience applying state-of-the-art optimization techniques for training and inference software to improve 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 retention schedules.
• Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms.

Role details

• Location: New York, NY (onsite)
• Compensation: USD 215,200 - 245,600 per year
• Team: Intelligent Foundations and Experiences (IFX)

Technologies you may work with

• 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