Nvidia Solutions Architect Generative AI Agents Data Processing 

United States, California 

572115308

What we need to see:

• Strong foundational expertise, from a BS, MS, or Ph.D. degree in Engineering, Mathematics, Physics, Computer Science, Data Science, or similar (or equivalent experience).

• 5+ years experience demonstrating an established track record in Deep Learning and Machine Learning. Strong software engineering and debugging skills, including experience with Python, C/C++, and Linux. Experience with GPUs as well as expertise in using deep learning frameworks such as TensorFlow or PyTorch.

• Real-world development of agentic RAG systems, built with frameworks such as LangGraph, LlamaIndex, CrewAI, etc.

• Strong background with vector databases (e.g., Pinecone, FAISS, or Milvus) and advanced indexing techniques, including k-nearest neighbors (KNN) and approximate nearest neighbor (ANN) search, to efficiently manage and query high-dimensional data.

• Ability to multitask effectively in a dynamic environment, as well as clear written and oral communications skills with the ability to effectively collaborate with executives and engineering teams.

Ways to stand out from the crowd:

• Hands-on experience with NVIDIA AI Enterprise Software (Morpheus, RAPIDS, NeMo and NIM) and AI infrastructure, including storage and networking (InfiniBand or Ethernet) knowledge. Expertise in DevOps/MLOps including Kubernetes, Docker, Helm charts, Jupyter notebooks.

• Proven experience in curating, collecting, and preprocessing large-scale multi-modal datasets using SOTA models and techniques.

• Experience with building and taking AI applications into production on cloud environments (e.g., AWS, Azure, GCP) and on-premises infrastructure.

• Proven ability to build data preparation pipelines for multimodal models, including benchmarking, profiling, and optimization of innovative algorithms.

• Extremely motivated, highly passionate, and curious about new technologies.