AI Engineer 5
About The Position
Capital One is hiring an AI Engineer 5 for its Intelligent Foundations and Experiences (IFX) team, which builds and deploys proprietary AI solutions and platforms for use across the company. The team works with partners across Capital One to advance AI science and engineering and deliver responsible, scalable capabilities that support products used by millions of customers. In this role, you will partner with engineers, research scientists, technical program managers, and product managers to develop AI-powered products. You will design, build, test, deploy, and support foundational AI software and production systems, including foundation-model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, evaluation, experimentation, governance, and observability. You will apply open-source and SaaS technologies, develop optimization techniques for large-scale AI systems, and contribute to the technical vision and long-term roadmap for foundational AI at Capital One. The position also involves designing and optimizing multi-model orchestration pipelines, leading cost-performance governance reviews, guiding design councils or review boards, and mentoring Principal- and Manager-level AI engineers. The ideal candidate enjoys building high-quality systems, applying current research judiciously, solving ambiguous problems, and using deep engineering, mathematics, hardware, software, and AI expertise to identify optimization opportunities.
Requirements
- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field and at least 6 years of experience developing AI and ML algorithms or technologies; or a master's degree in one of these fields and 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 systems 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 an 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, vector databases, guardrails, and 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 up with AI research and systems and applying novel techniques in production.
- Excellent communication and presentation skills, including the ability to explain complex AI concepts to peers.
- Experience architecting and integrating heterogeneous AI systems, including rule-based, retrieval-augmented, and generative 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 inputs and outputs.
Responsibilities
- Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.
- 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 and introduce foundation-model optimization techniques to improve the scalability, cost, latency, and throughput of 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 support technical consistency and compliance with AI engineering standards.
- Mentor Principal- and Manager-level AI engineers, fostering cross-domain learning and organizational technical maturity.
Benefits
- Comprehensive health, financial, and other benefits supporting employee well-being; eligibility varies by employment status and management level.
- 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
