Solutions Architect, Generative AI Agents and Data Processing

Do you want to be part of the team that brings Artificial Intelligence (AI) emerging technology to the field? We are looking for Solution Architects to join the NVIDIA AI Enterprise (NVAIE) SA Segment Team. We specialize on the newest technology and advances in Machine Learning, Deep Learning, Accelerated Data Analytics and Cloud. The vision of the NVAIE Segment team is to use our deep expertise to guide and enable the successful adoption at scale of NVIDIA AI Enterprise Software in production!

The Gen AI Data Processing team mission is to deliver innovative and efficient solutions that help enterprises lower costs through the use of our GPU data processing capabilities. We showcase the power of GPU processing through our knowledge of large scale data streaming with Morpheus, machine learning with RAPIDS, and distributed computing through Dask and Spark-RAPIDS. We enable accelerated data extraction and curation tools, including NV-Ingest and NeMo Data Curator to ensure the highest quality data for retrieval and generation using AI models. We use these tools to enhance performance in the context of LLM training data preparation and Retrieval-Augmented Generation (RAG) pipelines. With high quality datasets and Gen AI models, we also develop and optimize agent-based systems to integrate data from multiple sources and deliver accurate responses for complex queries.

What you’ll be doing:

A huge part of our work involves developing end-to-end Machine Learning and Deep Learning solutions for enterprise use cases. We help customers adopt NVIDIA AI SDKs and APIs by offering deep technical expertise and designing GPU-accelerated data processing pipelines that optimize compute resource utilization and improve workload performance for customers and partners. We provide feedback from these first-time implementations to improve our software products and scale knowledge by educating vertical teams and building communities on NVIDIA AI software products!

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.