AI Engineer 5 (AI Foundations, LLM Core and Agentic AI)

Sourced from jobs.equest.com

Capital One's Intelligent Foundations and Experiences (IFX) team is hiring a senior AI engineer to build and deploy production AI systems spanning foundation model training, LLM inference, agentic workflows, vector search, guardrails, and evaluation. The role combines hands-on engineering with technical leadership, driving cost-performance governance and multi-model orchestration across large-scale AI infrastructure. You will mentor principal and manager-level engineers and help shape the long-term roadmap for foundational AI at Capital One.

• Design, develop, test, deploy, and support AI software components including foundation model training, LLM inference, agents, similarity search, guardrails, evaluation, and observability
• Optimize foundation models and inference pipelines for scalability, cost, latency, and throughput
• Design and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models
• Lead cost-performance governance reviews tracking GPU utilization, model throughput, and inference cost efficiency
• Lead design councils and review boards to ensure technical consistency and AI engineering standards
• Mentor principal and manager-level AI engineers and elevate organizational technical maturity

• Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related field plus at least 6 years of experience developing AI/ML algorithms or technologies, or Master's degree plus at least 4 years
• At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java
• Experience deploying scalable AI solutions on cloud platforms such as AWS, Google Cloud, or Azure
• Strong foundation in engineering and mathematics with expertise across hardware, software, and AI

• Experience leading AI system development with tradeoff decisions around cost, latency, throughput, and accuracy
• Experience building agentic AI systems and agentic workflows
• Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
• Experience architecting heterogeneous AI systems combining rule-based, retrieval-augmented, and generative components
• Experience defining standards for ethical AI deployment including explainability, fairness, and human-in-the-loop review
• Experience right-sizing models, instance counts, and hardware types for context length and token requirements