Sr. Lead AI Engineer (AI Foundations)
$250,800–$286,200 year
On-siteNew York City, New York, United States or San Francisco, California, United States
Job Summary
Partner with cross-functional teams to deliver AI-powered products that reshape customer interactions and internal workflows. Design, develop, test, and deploy AI software components covering foundation model training, large language model inference, similarity search, guardrails, and observability. Leverage Open Source and SaaS technologies including AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch to build scalable, high-performance systems. Invent and introduce state-of-the-art LLM optimization techniques to improve scalability, cost, latency, and throughput for production AI infrastructure. Contribute to the long-term technical vision and roadmap for foundational AI systems at Capital One. Work within the Intelligent Foundations and Experiences team to advance state-of-the-art science and engineering capabilities.
Required Qualifications
- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields
- At least 6 years of experience developing AI and ML algorithms or technologies
- At least 6 years of experience programming with Python, Go, Scala, or Java
- Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields
- At least 4 years of experience developing AI and ML algorithms or technologies
Desired Qualifications
- 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
- Experience designing, developing, integrating, delivering, and supporting complex AI systems
- Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders
- Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang
- Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
- Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production
- Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers
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