AI Engineer 4
About The Position
Capital One is hiring an AI Engineer for its Intelligent Foundations and Experiences (IFX) team. The team develops proprietary AI solutions, models, and platforms that support products across Capital One and provide scalable value to customers. This role partners with engineers, research scientists, technical program managers, and product managers to develop AI-powered products for associates and customers. The position involves designing, developing, testing, deploying, and supporting AI software components, including foundation-model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. The engineer will use open-source and SaaS technologies such as AWS Ultraclusters, Hugging Face, vector databases, and PyTorch, and develop optimization techniques to improve production AI system scalability, cost, latency, and throughput. The engineer will contribute to the technical vision and roadmap for foundational AI systems, own architecture for complex AI systems, define and maintain reliability objectives, optimize GPU/TPU use and inference pipelines, and lead technical reviews to ensure security, data governance, and compliance. The role also includes mentoring colleagues on scalable design, performance tuning, and moving 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 relevant experience.
- At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.
Nice To Haves
- Experience leading development of AI systems and making tradeoff decisions involving cost, latency, throughput, and accuracy.
- 6+ 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 for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
- Experience building agentic AI systems and workflows.
- Passion for keeping current with AI research and systems 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 such as AWS Ultraclusters, Hugging Face, vector databases, and PyTorch.
- Develop foundation-model optimization techniques to improve scalability, cost, latency, and throughput in production AI systems.
- Contribute to the technical vision and long-term roadmap of foundational AI systems.
- Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment.
- Define and maintain service-level objectives for AI reliability, 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
- Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
- A comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
