AI Engineer 4 (Gen AI Platform Services: Agentic Systems)

Capital One • San Francisco, CA

7d

About The Position

Capital One’s Intelligent Foundations and Experiences (IFX) team develops proprietary AI models and platforms that enable teams across the company to deliver responsible, scalable AI capabilities and customer experiences. The AI Engineer will work with engineers, research scientists, technical program managers, and product managers to build AI-powered products for associates and customers.

The role involves designing, developing, testing, deploying, and supporting 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. The engineer will use open-source and SaaS technologies and develop optimization techniques to improve the scalability, cost, latency, and throughput of production AI systems. The position also contributes to the technical vision and long-term roadmap for Capital One’s foundational AI systems.

The engineer owns end-to-end architecture for complex AI systems, maintains reliability objectives, collaborates on GPU/TPU utilization and inference pipeline optimization, and leads technical reviews of deployments to ensure security, data governance, and compliance. The role also includes mentoring Principal and Senior Associates on scalable design, performance tuning, and translating research into production.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field and at least 4 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of these fields and at least 2 years of such experience.
• At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.
• Strong foundation in engineering and mathematics, with technical expertise spanning hardware, software, and AI.
• Ability to understand scientific publications and apply relevant techniques in production.
• Ability to adapt quickly, clarify ambiguous problems, investigate root causes, and communicate findings clearly.
• Passion for building high-quality systems and doing the right thing.

Nice To Haves

• Experience leading AI systems development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• 6 or more 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 AI services.
• Experience developing AI and ML algorithms or technologies such as LLM inference, similarity search and vector databases, 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 workflows.
• Passion for following AI research and systems developments and applying novel techniques judiciously in production.
• Proficiency in designing distributed systems for model training, evaluation, and online inference at petabyte scale.
• Experience defining AI model-governance processes, including producibility, lineage tracking, and automated retraining schedules.
• Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms.

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, vector databases, and PyTorch.
• Develop and introduce foundation-model optimization techniques to improve production-system scalability, cost, latency, and throughput.
• Contribute to the technical vision and long-term roadmap for foundational AI systems.
• Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment.
• Define and maintain AI reliability service-level objectives, including latency, uptime, and model-performance drift.
• Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate model-inference pipelines.
• Lead cross-functional technical reviews for AI system deployments, ensuring security, data governance, and compliance standards are met.
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• 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