9 hours ago
Remote, AustraliaSenior
Responsibilities
- Serve as a trusted technical advisor and lead customer and partner engagements focused on NVIDIA AI platforms.
- Co-design scalable architectures, technical direction, integration milestones, and deployment plans with enterprise customers and partners.
- Build and run proofs of concept and demonstrations, including Python-based data pipelines, AI/deep-learning model training, and container-based deployments.
- Develop GenAI full-stack solutions with NVIDIA engineering, product, business, and sales teams.
- Troubleshoot complex technical issues, advocate for customer needs, and incorporate customer feedback into product development.
- Create training, whitepapers, blogs, wiki articles, and knowledge-transfer sessions for customers, partners, and Solutions Architects.
- Coordinate technical projects, stakeholders, resources, and strategic engagements across internal and external organizations.
Requirements
- At least 5 years of experience in deep learning, data science, or software development, including knowledge of GPU-based parallel computing.
- Professional experience as a customer-facing Data Scientist or in AI, deep learning, machine learning, computer vision, conversational AI, or data analytics projects.
- Bachelor’s, master’s, or doctoral degree in Computer Science, Electrical or Computer Engineering, Physics, Mathematics, another engineering field, or equivalent experience.
- Strong customer-facing, presales, consulting, and technical experience with AI, machine learning, and big-data frameworks.
- Strong experience building RAG workflows and Agentic AI applications, including developer experience with LangChain and LangGraph.
- Experience with Kubernetes, data-center compute, storage, and networking environments.
- Experience leading technical teams or advising customers and partners in AI is preferred.
- Experience with NVIDIA NeMo and NIM, machine-learning/deep-learning frameworks, MLOps tools, cloud and on-premises solutions, APIs, storage, and data migration is preferred.
- Strong planning, project coordination, communication, presentation, collaboration, and knowledge-sharing skills.
Tech Stack
Categories
Solutions Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.