AI Engineer 5 (AI Foundations: Evaluations and Agentic Workflows)
About The Position
Capital One’s Intelligent Foundations and Experiences (IFX) team builds and deploys proprietary AI solutions and platforms that help teams across the company improve products and customer experiences responsibly and at scale. The AI Engineer 5 partners with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers. The role involves developing and supporting foundational AI systems, including foundation-model training, large-language-model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It also involves applying open-source and SaaS AI technologies, developing optimization techniques for production AI systems, and contributing to the technical vision and long-term roadmap for foundational AI at Capital One. This position designs and optimizes multi-model orchestration pipelines and leads cost-performance governance and technical design reviews. It also mentors Principal- and Manager-level AI engineers. The position is designated hybrid and requires adherence to Capital One’s hybrid policy for on-site presence at the designated office. The role may be eligible for performance-based incentive compensation, including cash bonuses and/or long-term incentives.
Requirements
- Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, plus at least 6 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of those fields, plus at least 4 years of such experience.
- At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.
Nice To Haves
- Experience leading AI system development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
- At least 7 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 complex AI systems.
- 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.
- Interest in keeping current with AI research and systems and applying novel techniques judiciously in production.
- Excellent communication and presentation skills, including the ability to explain complex AI concepts to peers.
- Experience architecting and integrating rule-based, retrieval-augmented, and generative AI components into unified production pipelines.
- Experience defining and enforcing ethical AI deployment standards, including explainability, fairness, and human-in-the-loop review processes.
- Demonstrated ability to balance model performance and operational cost using dynamic inference strategies and model compression.
- Experience right-sizing models, instance counts, and hardware types based on requirements such as context length and token input and output volumes.
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 foundation-model optimization techniques to improve scalability, cost, latency, and throughput in production AI systems.
- Contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One.
- Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models.
- Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency.
- Lead team design councils or design review boards to promote technical consistency and compliance with AI engineering standards.
- Mentor Principal- and Manager-level AI engineers and foster cross-domain learning and organizational technical maturity.
Benefits
- Comprehensive health, financial, and other benefits supporting total well-being.
- Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
