AI Engineer 3 (AI Foundations)

About the Role

At Capital One, we are creating responsible and reliable AI systems, changing banking for good. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life.

Key Responsibilities

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• 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.
• Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems.
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
• Lead development and benchmarking of multi-turn conversational and tool-using agent workflows, ensuring measurable performance and safety metrics.
• Implement scalable pipelines for training, fine-tuning and deploying foundation or domain-specific models across multiple environments.
• Collaborate with research and data engineering teams to curate high-quality datasets and improve model evaluation methodologies.
• Contribute to governance and security efforts for model traceability, lineage documentation, and version control of deployed AI assets.
• Mentor junior AI engineers and advocate for engineering excellence, reproducibility, and responsible experimentation.

Requirements & Tech Stack

• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.

Qualifications

Basic Qualifications:

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 3 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 1 year of experience developing AI and ML algorithms or technologies.
• At least 3 years of experience programming with Python, Go, Scala, CUDA, or Java.

Preferred Qualifications:

• Experience contributing to development of components of AI systems with tradeoff decisions around cost, latency, throughput, and accuracy.
• 4 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud).
• Experience developing, delivering, and supporting AI services.
• 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 developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
• Experience in building agentic AI systems and agentic workflows.
• Demonstrated ability to evaluate and optimize LLM performance using quantitative metrics (accuracy, coherence, latency, cost).
• Hands-on experience implementing retrieval-augmented generation (RAG), vector database integrations, and fine-tuning workflows.
• Experience applying prompt-engineering strategies, safety guardrails, and red-teaming methodologies to production AI systems.