AI Engineer (LLM, Agentic AI)

Requirements:

• We require a bachelors degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related discipline, 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 while balancing cost, latency, throughput, and accuracy.
• 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, building, delivering, and supporting complex AI systems.
• We prefer experience developing AI and ML technologies such as LLM inference, similarity search, VectorDBs, guardrails, and 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, lower latency, higher throughput, and reduced cost.
• We prefer experience building agentic AI systems and multi-agent workflows.
• We prefer strong communication and presentation skills with the ability to explain complex AI concepts to peers.
• We prefer experience architecting and integrating heterogeneous AI systems, including rule-based, retrieval-augmented, and generative components, into unified production pipelines.
• We prefer experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review.
• We prefer a demonstrated ability to balance model performance with operational 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, token inputs, and token outputs.

Responsibilities:

• We partner with cross-functional teams 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 scalability, cost efficiency, latency, and throughput in large-scale production AI systems.
• We help shape the technical vision and long-term roadmap for foundational AI systems at Capital One.
• We design, implement, and optimize multi-model orchestration pipelines that integrate LLMs, vector search, and domain-specific models into unified systems.
• We lead cost-performance governance reviews across AI systems by tracking GPU utilization, model throughput, and inference cost efficiency.
• We lead team design councils and design review boards to maintain technical consistency and alignment with AI engineering standards.
• We mentor principal- and manager-level AI engineers to promote cross-domain learning and strengthen overall technical maturity.

Technologies:

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

More

At Capital One, we are building responsible and reliable AI systems to change banking for good. Our Intelligent Foundations and Experiences team sits at the center of that vision, working closely with partners across the company to advance AI science and engineering and to build proprietary solutions that serve millions of customers. We invest deeply in technology infrastructure and world-class talent, and our AI models and platforms help teams across Capital One enhance their products in responsible, scalable ways. This role includes location-based salary ranges for Cambridge, MA; McLean, VA; New York, NY; and San Jose, CA, and is also eligible for performance-based incentive compensation and a comprehensive benefits package supporting total well-being. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

last updated 43 week of 2026