AI Engineer 5 (MLX, Agentic AI, Gen AI platform Services)

Capital One’s Intelligent Foundations and Experiences (IFX) team is building responsible, scalable AI systems and the platform components that power foundation model experiences. As an AI Engineer 5, you will help design, deploy, and optimize AI software that supports how associates work and how customers interact with Capital One, with a focus on foundation model training, inference, agentic workflows, and production governance.

What you’ll do

• Work with a cross-functional group including engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.
• Design, develop, test, deploy, and support AI components such as foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
• Apply a broad stack of open source and SaaS AI technologies including AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch.
• Introduce state-of-the-art foundation model optimization techniques to improve production performance, including scalability, cost, latency, and throughput.
• Help shape the technical vision and long term roadmap for foundational AI systems at Capital One.
• Design, implement, and optimize multi-model orchestration pipelines that integrate LLMs, vector search, and domain-specific models into unified systems.
• Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency.
• Lead team design councils or design review boards to support technical consistency and compliance with AI engineering standards.
• Mentor Principal and Manager-level AI engineers to encourage cross-domain learning and raise organizational technical maturity.

Location and work setting

• New York, NY (onsite)

Salary

USD 250,800 - 286,200 per year (New York, NY)

Key qualifications

• Education and experience: Bachelor’s degree in Computer Science/AI/Electrical Engineering/Computer Engineering (or related field) plus at least 6 years developing AI and ML algorithms or technologies, or a Master’s degree plus at least 4 years developing AI and ML algorithms or technologies.
• Programming: At least 6 years programming with Python, Go, Scala, CUDA, or Java.

Technologies you may work with

• AWS Ultraclusters, Huggingface, VectorDBs, PyTorch
• AWS, Google Cloud, Azure
• Python, Go, Scala, CUDA, Java
• LLMs, vector search, similarity search, guardrails, retrieval-augmented approaches
• Rule-based components, foundation model workflows, multi-agent workflows, agents, observability

Additional qualifications

• Experience leading development AI systems with tradeoff decisions across cost, latency, throughput, and accuracy.
• 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (for example: AWS, Google Cloud, Azure, or equivalent private cloud).
• Experience designing, developing, delivering, and supporting complex AI systems.
• Experience developing AI and ML algorithms or technologies (for example: LLM inference, similarity search and VectorDBs, guardrails, memory) using Python, C++, C#, Java, CUDA, or Golang.
• Experience optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and agentic workflows.
• Excellent communication and presentation skills for articulating complex AI concepts.
• Experience architecting and integrating heterogeneous AI systems, including rule-based, retrieval-augmented, and generative components, into unified production pipelines.
• Experience defining and enforcing ethical AI deployment standards, including explainability, fairness, and human-in-the-loop review processes.
• Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression.
• Experience right-sizing models, instance counts, and hardware types based on requirements such as context length and token inputs/outputs.

Compensation and benefits

• Performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.