AI Engineer 4 (MLX, Agentic AI, Gen AI workflows, GraphRAG)
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 deliver responsible, scalable AI capabilities. The AI Engineer 4 partners with engineers, research scientists, technical program managers, and product managers to create AI-powered products for associates and customers. The role develops and supports AI software components spanning foundation-model training, large-language-model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It works with open-source and SaaS AI technologies and develops optimization techniques to improve the scalability, cost, latency, and throughput of production AI systems. The engineer contributes to the technical vision and long-term roadmap for foundational AI systems, owns architecture for complex AI systems, and ensures maintainability, observability, and ethical alignment. The role also defines reliability objectives, works with infrastructure engineering to improve GPU/TPU utilization and inference pipelines, leads deployment reviews covering security and compliance, and mentors Principal and Senior Associates. The position offers a salary range that varies by listed location and may include performance-based incentives.
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/ML algorithms or technologies; or a master’s degree in one of those fields and at least 2 years of such experience.
- At least 4 years of programming experience with Python, Go, Scala, CUDA, or Java.
- Strong engineering and mathematics foundation, with expertise in hardware, software, and AI.
- Ability to understand scientific publications and apply suitable novel techniques in production.
- Ability to adapt to ambiguous problems, investigate root causes, and communicate findings clearly.
- Passion for building high-quality systems and pursuing responsible outcomes.
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/ML algorithms or technologies such as LLM inference, similarity search, 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 keeping up with AI research and systems and applying suitable novel techniques 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 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, VectorDBs, and PyTorch.
- Develop and introduce foundation-model optimization techniques to improve production system scalability, cost, latency, and throughput.
- Contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One.
- Own end-to-end architecture for complex AI systems, ensuring maintainability, observability, and ethical alignment.
- Define and maintain AI reliability service-level objectives, 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 AI deployments, ensuring security, data governance, and compliance standards are met.
- Mentor Principal and Senior Associates in scalable design, performance tuning, and translating research into production.
Benefits
- Comprehensive health, financial, and other benefits supporting total 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
