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

Capital One • San Jose, CA

1d

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI solutions that support products across the company and deliver value to millions of customers. The team works with partners across Capital One to advance AI science and engineering and to deliver responsible, scalable AI capabilities.

The AI Engineer will partner with engineers, research scientists, technical program managers, and product managers to build AI-powered products. The role covers the design, development, testing, deployment, and support of AI software components, including foundation model training, large language model inference, agent and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The engineer will use open-source and SaaS technologies, develop optimization techniques for production AI systems, and contribute to the technical vision and roadmap for foundational AI systems.

The position also involves designing and optimizing multi-model orchestration pipelines; leading cost-performance governance reviews; and leading design councils or review boards to promote technical consistency and compliance with AI engineering standards. The role includes mentoring Principal- and Manager-level AI engineers and helping raise organizational technical maturity.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field and at least 6 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of those fields and at least 4 years of that experience.
• At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.

Nice To Haves

• Experience leading AI systems development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• 7+ years of experience deploying scalable and responsible AI solutions on cloud platforms such as 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, such as LLM inference, similarity search and VectorDBs, guardrails, or memory, using Python, C++, C#, Java, CUDA, or Golang.
• Experience developing and applying state-of-the-art techniques to optimize training and inference software for hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and agentic workflows.
• Passion for following AI research and systems developments and applying novel techniques judiciously in production.
• Excellent communication and presentation skills, including the ability to explain complex AI concepts to peers.
• Experience architecting and integrating heterogeneous AI systems, including rule-based, retrieval-augmented, and generative components, into unified production pipelines.
• Experience defining and enforcing standards for ethical AI deployment, 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 and outputs.

Responsibilities

• Partner with 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.
• Use open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch.
• Invent and introduce foundation-model optimization techniques to improve scalability, cost, latency, and throughput in production AI systems.
• Contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One.
• Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models.
• Establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency.
• Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards.
• Mentor Principal- and Manager-level AI engineers, fostering cross-domain learning and organizational technical maturity.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Eligibility to earn performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior