AI Engineer 4 (AI Foundations)
About The Position
Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI solutions and platforms used across the company. The team partners across Capital One to advance AI science and engineering and to deliver responsible, scalable AI capabilities that improve products and experiences for customers and associates. The AI Engineer will work with engineers, research scientists, technical program managers, and product managers to develop AI-powered products and foundational AI systems. The work spans 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, develop optimization techniques for large-scale production AI systems, contribute to technical direction and roadmaps, and own architecture for complex AI systems. The role also includes defining reliability objectives, optimizing accelerator utilization and inference pipelines, leading technical deployment reviews, and mentoring colleagues.
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; alternatively, 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 expertise in hardware, software, and AI.
- Ability to understand scientific publications and apply novel techniques in production.
- Ability to adapt quickly, clarify ambiguous problems, investigate root causes, communicate findings clearly, and share new ideas.
Nice To Haves
- Experience leading AI system development 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 VectorDBs, guardrails, and memory—using Python, C++, C#, Java, CUDA, or Golang.
- Experience developing and applying state-of-the-art training and inference optimization techniques to improve hardware utilization, latency, throughput, and cost.
- Experience building agentic AI systems and workflows.
- Passion for staying current with AI research and systems and applying novel techniques judiciously in production.
- Proficiency 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, VectorDBs, and PyTorch.
- Develop foundation-model optimization techniques to improve production-system scalability, cost, latency, and throughput.
- Contribute to the technical vision and long-term roadmap of foundational AI systems at Capital One.
- 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 new AI system deployments and ensure 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.
- Comprehensive health, financial, and other benefits supporting total well-being.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
