AI Engineer 4

About the Role

At Capital One, we are creating responsible and reliable AI systems, changing banking for good. Our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams to deliver breakthrough product experiences and scalable, high-performance AI infrastructure. You will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses.

The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways.

Key Responsibilities

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• 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.
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
• Invent and introduce state-of-the-art foundation model optimization techniques to improve performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and long-term roadmap of foundational AI systems.
• Own the end-to-end architecture for complex AI systems, ensuring 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, ensuring security, data governance, and compliance standards are met.
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation.

Requirements & Tech Stack

• Python
• Go
• Scala
• CUDA
• Java
• AWS Ultraclusters
• Huggingface
• VectorDBs
• PyTorch
• C++ (preferred)
• C# (preferred)
• Golang (preferred)
• Cloud platforms (e.g., AWS, Google Cloud, Azure, or equivalent private cloud) (preferred)
• Agentic AI systems and agentic workflows (preferred)
• Distributed systems for model training, evaluation, and online inference (preferred)
• AI model governance processes (preferred)

Qualifications

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies.

OR

• Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 2 years of experience developing AI and ML algorithms or technologies.
• At least 4 years of experience programming with Python, Go, Scala, CUDA, or Java.
• Experience leading development AI systems with tradeoff decisions around cost, latency, throughput, and accuracy (preferred).
• 6 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) (preferred).
• Experience designing, developing, delivering, and supporting AI services (preferred).
• 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 (preferred).
• Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost (preferred).
• Experience in building agentic AI systems and agentic workflows (preferred).
• Passion for staying abreast of the latest AI research and AI systems, and judiciously applying novel techniques in production (preferred).
• Proficiency in designing distributed systems for model training, evaluation, and online inference at petabyte scale (preferred).
• Experience defining AI model governance processes, including producibility, lineage tracking, and automated retaining schedules (preferred).
• Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms (preferred).