Solution Architect, Financial Services

We are looking for an individual who can enable customer productivity and develop lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. Your duties will vary from working on proof-of-concept demonstrations to driving relationships with key technical leads and developers to ensure successful adoption of NVIDIA-based AI technology.

Key Responsibilities:

• FSI Strategic Engagement: Work with financial institutions and their technology ecosystem to leverage NVIDIA's advanced technologies.
• Algorithmic & SDK Guidance: Guide customers in implementing next-generation model distillation, domain adaptation, reinforcement learning (RL), and post-training algorithms by helping them adopt NVIDIA AI SDKs and APIs.
• Collaborative Innovation: Work closely with product management, engineering, applied research, and sales teams to develop and deliver comprehensive solutions.
• Technical Advocacy: Improve NVIDIA products and build creative solutions to overcome scaling challenges at the intersection of computer architecture, libraries, and AI applications.
• Knowledge Sharing: Contribute to the wider organization and community by sharing your expert knowledge, contributing to open-source projects, or delivering hands-on training.
• Enterprise Impact: Be part of the team that helps bring NVIDIA technology to life in the Enterprise, acting as the face and trusted expert advisor that our customers and partners rely on.

Requirements:

• BS, MS, or PhD degree in Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related technical field.
• Financial Services Background: Proven experience working within Financial Services firms.
• AI Framework Expertise: 5+ years of experience with AI frameworks such as PyTorch, JAX, or TensorFlow, and libraries like Hugging Face Transformers.
• Programming Proficiency: Strong Python programming, software design, and debugging skills.
• Parallelism & CUDA: Deep understanding of distributed computing and parallelism methodologies (model and data parallelism) coupled with a good understanding or hands-on experience with CUDA.
• Model Lifecycle: Experience with AI model lifecycle components, including pre-training, supervised fine-tuning, and model evaluation.
• Adaptability: Capable of working in a constantly evolving environment and quickly adapting to change.
• Communication: Clear written and oral communication skills with the ability to effectively collaborate with executives and engineering teams.