AI Engineer 4 (AI Safety and Multimodal)

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Capital One's Intelligent Foundations and Experiences team is hiring a senior AI Engineer to build and deploy responsible, reliable AI systems at scale. The role focuses on AI safety and multimodal systems, spanning foundation model training, LLM inference, agentic workflows, guardrails, and model evaluation. You will own end-to-end architecture for complex AI systems 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, and observability
• Invent and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput of production AI systems
• Own end-to-end architecture for complex AI systems with maintainability, observability, and ethical alignment
• Define and maintain SLOs for AI reliability including latency, uptime, and model performance drift
• Collaborate with infrastructure engineering to optimize GPU/TPU utilization and accelerate inference pipelines
• Lead cross-functional technical reviews for new AI deployments ensuring security, data governance, and compliance
• Mentor Principal and Senior Associates on scalable design, performance tuning, and research-to-production translation

• Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related field plus at least 4 years of experience developing AI/ML algorithms or technologies, or Master's degree plus at least 2 years of experience
• At least 4 years of experience programming with Python, Go, Scala, CUDA, or Java
• Experience with AI red teaming and deploying safety guardrails for multimodal systems including VLMs, MLLMs, and MM-RAG architectures

• 6+ years deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, or Azure
• Experience leading AI system development with tradeoff decisions around cost, latency, throughput, and accuracy
• Experience with LLM inference, similarity search, VectorDBs, guardrails, and memory systems
• Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
• Experience in AI safety and LLM safety alignment
• Experience with safety and vulnerability assessment of multimodal applications using image/video plus text
• Experience building agentic AI systems and agentic workflows
• 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 retention schedules
• Demonstrated ability to influence architectural decisions across multiple AI product lines or platforms