Capital One
AI Engineer 4 (Gen AI Platform Services: Agentic AI)
$197K–$246K
New York, NY +4 · Full-time · Posted Oct 26, 2026
- Location
- New York, NY
San Francisco, CA
McLean, VA
Cambridge, MA
San Jose, CA - Experience
- At least 4 years developing AI/ML algorithms or technologies with a bachelor’s degree, or at least 2 years with a master’s degree; at least 4 years programming. Preferred: 6+ years cloud AI deployment and 3+ years Kubernetes.
- Education
- Bachelor's or Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Build and optimize scalable, responsible AI platforms and agentic systems that power products for Capital One associates and customers.
What you'll do
- Design, deploy, and support AI components, including foundation-model training, LLM inference, agents, evaluation, guardrails, and observability.
- Optimize production AI systems for scalability, cost, latency, throughput, and hardware utilization; own architecture and reliability objectives.
- Set technical direction and lead deployment reviews covering security, governance, compliance, and ethical alignment.
What they ask for
- Bachelor’s degree in a related field plus at least 4 years developing AI/ML algorithms or technologies, or a master’s degree in a related field plus at least 2 years in that area.
- At least 4 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development and making tradeoffs among cost, latency, throughput, and accuracy.
- 6+ years deploying scalable and responsible AI solutions on cloud platforms; 3+ years with Kubernetes.
Pay
$197,300 - $225,100 USD base salary (Cambridge, MA and McLean, VA); $215,200 - $245,600 USD base salary (New York, NY, San Francisco, CA, and San Jose, CA)
Tools
Python, Go, Scala, CUDA, Java, AWS Ultraclusters, Huggingface, PyTorch
AI focus
Develop foundation-model and LLM systems, agentic and multi-agent workflows, and scalable AI infrastructure; optimize training and inference and support evaluation, governance, and responsible deployment.
Model-written summary · the original posting is authoritative