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

Capital One is hiring an AI Engineer 5 to build and support AI-powered products and foundational AI systems in an onsite role in San Jose, CA.

Responsibilities

• Collaborate with cross-functional partners including 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, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability
• Apply Open Source and SaaS AI tooling such as AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch
• Develop and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput for large-scale production AI
• Contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One
• Design and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models
• Establish and lead cost-performance governance reviews across AI systems, including 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 support cross-domain learning and raise organizational technical maturity

Requirements

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

Technologies

• AWS Ultraclusters
• Huggingface
• VectorDBs
• PyTorch
• AWS, Google Cloud, Azure
• Python, Go, Scala, CUDA, Java, C++, C#, Golang
• LLMs, vector search, GPU utilization
• Rule-based, retrieval-augmented, generative components

Benefits

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

Team

• Intelligent Foundations and Experiences (IFX) team brings Capital One’s AI vision to life
• Works with partners across the company to advance state-of-the-art science and AI engineering
• Builds and deploys proprietary solutions central to the business, delivering value to millions of customers
• Enables teams across Capital One to enhance products with responsible, scalable AI

Ideal Candidate

• Enjoys building systems, taking pride in quality work, and doing the right thing
• Interested in problems that help change banking for good
• Stays current with AI research, can interpret scientific publications, and applies novel techniques in production
• Thrives on bringing clarity to big, undefined problems
• Asks questions, digs to find root causes, and communicates findings clearly
• Shares new ideas even when they are unproven
• Deeply technical with strong engineering and mathematics foundations
• Understands optimization opportunities across hardware, software, and AI
• Resilient trailblazer able to forge new paths toward business goals when the route is unknown

Preferred 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 (AWS, Google Cloud, Azure, or equivalent private cloud)
• Experience designing, developing, delivering, and supporting complex AI systems
• Experience developing AI/ML algorithms such as LLM inference, similarity search and VectorDBs, guardrails, and memory using Python, C++, C#, Java, CUDA, or Golang
• Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
• Experience building agentic AI systems and agentic workflows
• 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 (explainability, fairness, and human-in-the-loop review processes)
• Ability to balance model performance and operational cost using dynamic inference and model compression
• Experience right-sizing models, instance counts, and hardware types based on requirements such as context length and token inputs/outputs

Salary and Location

• Location: San Jose, CA (onsite)
• Salary: USD 250,800 - 286,200 per year