Capital One
AI Engineer 5 (Gen AI SDK: Agentic AI, Guardrails, Evaluation)
$230K–$286K
New York, NY +4 · Full-time · Posted Oct 16, 2026
- Location
- New York, NY
San Francisco, CA
McLean, VA
Cambridge, MA
San Jose, CA - Experience
- At least 6 years developing AI/ML algorithms or technologies with a bachelor's degree, or at least 4 years with a master's degree; at least 6 years programming. Preferred: 7+ years deploying scalable and responsible AI solutions on cloud platforms.
- 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 responsible, scalable AI systems and agentic workflows for Capital One products and enterprise platforms.
What you'll do
- Design, develop, deploy, and support AI components including foundation model training, LLM inference, agents, guardrails, evaluation, and observability.
- Optimize large-scale AI systems and multi-model orchestration for scalability, cost, latency, throughput, and hardware utilization.
- Shape technical roadmaps and standards, lead design reviews and governance, and mentor AI engineers.
What they ask for
- Bachelor's degree in a relevant field plus at least 6 years developing AI/ML algorithms or technologies, or a master's degree in a relevant field plus at least 4 years doing so.
- At least 6 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development and making cost, latency, throughput, and accuracy tradeoffs.
- Experience deploying scalable, responsible AI solutions on cloud platforms; agentic AI, AI optimization, and complex system integration experience are also listed.
Pay
$229,900 - $262,400 USD base salary (Cambridge, MA and McLean, VA); $250,800 - $286,200 USD base salary (New York, NY, San Francisco, CA, and San Jose, CA)
Tools
Python, Go, Scala, CUDA, Java, AWS Ultraclusters, Huggingface, VectorDBs, PyTorch
AI focus
Foundation models, LLM inference, agentic and multi-agent workflows, similarity search, guardrails, model evaluation, orchestration, and optimization of AI training and inference.
Model-written summary · the original posting is authoritative