Staff AI Engineer - Enterprise Analysis Platform (Remote Eligible)
$245k - $279k • Remote • San Jose, CA • Full-time
Posted 1mo ago
About the job
Capital One is building a new AI-native platform to empower business users to execute AI-assisted analysis across various lines of business. This enterprise platform will serve over 15,000 users, integrating deterministic code with reasoning systems and both frontier and homegrown AI models to transform data analysis within the company. The role involves partnering with cross-functional teams to deliver AI-powered products, designing and developing AI software components, and leveraging a broad stack of open-source and SaaS AI technologies. You will invent and introduce state-of-the-art foundation model optimization techniques to enhance the performance of large-scale production AI systems, contribute to the technical vision and roadmap of foundational AI systems, and set the technical direction for enterprise-wide AI architecture.
Responsibilities
- Partner with a cross-functional team to deliver AI-powered products.
- 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.
- Leverage Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch.
- Invent and introduce state-of-the-art foundation model optimization techniques to improve performance (scalability, cost, latency, throughput) of large-scale production AI systems.
- Contribute to the technical vision and long-term roadmap of foundational AI systems.
- Set the technical direction for enterprise-wide AI architecture, unifying tooling, observability, and deployment standards.
- Own the design and integration of model routing, caching, and orchestration systems for hybrid and multi-model workloads.
- Champion responsible AI principles across all system design, embedding transparency, reproducibility, and fairness-by-design.
- Drive internal education, mentorship, and dissemination of best practices through architecture councils and AI guilds.
Requirements
- Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree plus at least 6 years of experience.
- At least 8 years of experience programming with Python, Go, Scala, CUDA, or Java.
- Experience designing AI systems with tradeoff decisions around cost, latency, throughput, and accuracy.
- 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud).
- Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems.
- Demonstrated ability to lead and mentor multiple engineering teams 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, CUDA, or Golang.
- Experience developing and applying state-of-the-art techniques for optimizing training and inference software.
- Experience in building agentic AI systems and agentic workflows.
- 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.
- Proven track record of defining and operationalizing enterprise AI architecture standards, including data pipeline governance, observability, and evaluation frameworks.