Solutions Architect – Generative AI Agents and Data Processing
| Company | NVIDIA |
|---|---|
| Location | Santa Clara, CA, USA |
| Salary | $148000 – $235750 |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s, PhD |
| Experience Level | Senior |
Requirements
- 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.
- 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.
Responsibilities
- Developing end-to-end Machine Learning and Deep Learning solutions for enterprise use cases.
- Helping customers adopt NVIDIA AI SDKs and APIs by offering deep technical expertise.
- Designing GPU-accelerated data processing pipelines that optimize compute resource utilization and improve workload performance for customers and partners.
- Providing feedback from first-time implementations to improve software products.
- Educating vertical teams and building communities on NVIDIA AI software products.
Preferred Qualifications
- 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.