AI Engineer: AI Foundations & VLM Customization

Requirements:

• We require a bachelors degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, plus at least 6 years of experience developing AI and ML algorithms or technologies, or a masters degree in one of those fields plus at least 4 years of relevant experience.
• We require at least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.
• We prefer candidates with experience leading AI system development where cost, latency, throughput, and accuracy tradeoffs must be balanced.
• We prefer 7 years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud environments.
• We prefer experience designing, developing, delivering, and supporting complex AI systems.
• We prefer experience building AI and ML algorithms or technologies such as LLM inference, similarity search, vector databases, guardrails, or memory using Python, C++, C#, Java, CUDA, or Golang.
• We prefer experience applying advanced techniques to optimize training and inference software for better hardware utilization, latency, throughput, and cost.
• We prefer experience creating agentic AI systems and agentic workflows.
• We prefer strong communication and presentation skills, with the ability to explain complex AI concepts clearly to peers.
• We prefer experience architecting and integrating mixed AI systems, including rule-based, retrieval-augmented, and generative components, into production pipelines.
• We prefer experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review.
• We prefer the ability to balance model performance and operating cost through dynamic inference strategies and model compression.
• We prefer experience right-sizing models, instance counts, and hardware types based on requirements such as context length and token volumes.

Responsibilities:

• We partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that improve how our associates work and how our customers interact with Capital One.
• We 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.
• We use a broad set of open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, VectorDBs, PyTorch, and related tools.
• We invent and introduce advanced foundation model optimization techniques to improve the scalability, cost, latency, and throughput of large-scale production AI systems.
• We contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One.
• We design, implement, and optimize multi-model orchestration pipelines that unify LLMs, vector search, and domain-specific models.
• We establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency.
• We lead design councils or design review boards to support technical consistency and alignment with AI engineering standards.
• We mentor Principal- and Manager-level AI engineers, encourage cross-domain learning, and help raise the organizations technical maturity.

Technologies:

• Agentic AI
• AI
• AWS
• Azure
• C#
• Cloud
• CUDA
• Golang
• Hardware
• Support
• Java
• LLM
• Model Training
• PyTorch
• Python
• Scala
• Machine Learning

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At Capital One, we are building responsible and reliable AI systems that are changing banking for good. Our Intelligent Foundations and Experiences team sits at the center of our AI vision, working closely with partners across the company to advance science and AI engineering and to build proprietary solutions that deliver value to millions of customers. We empower teams across Capital One with scalable, responsible AI platforms and models, and this role is based in locations including Cambridge, McLean, New York, and San Jose. The position is eligible for performance-based incentive compensation and a comprehensive, competitive, and inclusive benefits package that supports total well-being. We may also consider sponsoring a new qualified applicant for employment authorization for this position.

last updated 43 week of 2026