About the job
NVIDIA is seeking outstanding AI Solutions Architects to assist and support customers that are building solutions with our newest AI technology. At NVIDIA, our solutions architects work across different teams and enjoy helping customers with the latest Accelerated Data Analytics and Deep Learning software and hardware platforms. We're looking to grow our company, and build our teams with the smartest people in the world. Would you like to join us at the forefront of technological advancement?
Responsibilities
Drive technical GTM with Data Platform ISVs across query engines, databases, analytics platforms, data processing frameworks, and AI data infrastructure.
Partner with ISVs on discovery, architecture reviews, technical deep dives, POCs, benchmarks, demos, and customer-facing enablement
Help ISVs identify the right NVIDIA acceleration paths for their platforms and use cases, including cuDF, Spark RAPIDS, Polars, Velox, cuVS, and related NVIDIA libraries
Build repeatable GTM assets such as reference architectures, technical playbooks, demos, blogs, talks, and customer training
Support emerging data platform use cases for GenAI, including unstructured data processing, RAG pipelines, data preparation, and retrieval workflows
Qualifications
Minimum
BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience)
8+ years of hands-on experience with Machine Learning, Deep Learning and Data Analytics
Strong background in data platforms, distributed systems, analytics, databases, or systems for managing and processing data
Familiarity with data ecosystems such as Spark, Pandas, Polars, DuckDB, Trino, Presto, Velox, vector databases, or unstructured data pipelines
Experience working with ISVs, partners, or enterprise customers in a solutions architecture or field engineering role
Excellent presentation, communication and collaboration skills
Preferred
Hands-on experience with NVIDIA GPUs and software libraries, such as NeMo Retriever, cuVS, RAPIDS and cuDF
Background in RAG, agentic AI, unstructured data processing, or inference and data platform integration
Excellent C/C++ programming skills, including debugging, profiling, code optimization, performance analysis, and test design
Familiarity with parallel programming and distributed computing platforms