Solutions Architect, Financial Services - Data Center and Infrastructure
- Financial Services Engagement: Work directly with trading firms and banks to leverage NVIDIA’s advanced technologies for financial workloads.
- Infrastructure Design: Implement sustainable data center architectures that optimize energy efficiency and reduce environmental impact.
- Technical Specialist: Serve as a technical specialist for GPU and networking products, collaborating closely with account managers to secure design wins.
- Collaboration: Work closely with product management, engineering, and sales teams to develop and deliver comprehensive AI and accelerated computing solutions.
- Industry Interaction: Dynamically engage with developers, industry researchers, data scientists, and IT managers to solve a range of technical challenges.
- RA Reviews: Lead technical project aspects of complex data center deployments, including the review and validation of Reference Architectures (RA) for large-scale financial infrastructure.
- BS, MS, or PhD degree in Machine Learning, Computer Science, or a related technical field.
- Financial Firm Experience: Proven experience working within Financial Services firms.
- Accelerated Computing: Minimum of 8 years of experience in AI and accelerated technologies.
- Pre-Sales Expertise: Proven experience driving the technical pre-sales process and engaging with customer engineers and architects.
- Large-Scale Systems: Proven experience with large-scale systems management and infrastructure automation.
- GPU Stack: Experience with NVIDIA GPUs and related software stacks, such as cuDNN and NCCL.
- Core Infrastructure: Strong knowledge of AI and data center technologies, including proficiency in Operating Systems and Linux kernel drivers.
- Communication: Solid written and oral communication skills with familiarity in collaborative environments.
- Cloud Platforms: Proficiency in cloud platforms (AWS, Azure, Google Cloud) and hybrid cloud solutions.
- Orchestration & Tooling: Knowledge of software-defined infrastructure, Kubernetes, and MLOps technologies.
- Advanced Cooling: Experience with liquid cooling technologies and practices.
- Systems & Networking: Hands-on experience with InfiniBand, NVIDIA Networking technologies (DPU, RoCE), and ARM CPU solutions.
- Programming: Experience with Python or C/C++ programming and AI workflow development (training/inference).
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