Senior Solutions Architect designing digital manufacturing solutions at LyondellBasell. Leading architecture for production, reliability, energy, and sustainability use cases with a focus on innovation.
About the role
- Solutions Architect at NVIDIA driving AI and ML solutions on cloud platforms. Collaborating with multi-functional teams and mentoring customers to improve GPU-enabled machine learning workflows.
Responsibilities
- Help cloud customers craft, deploy, and maintain scalable, GPU-accelerated inference pipelines on cloud ML services and Kubernetes for large language models (LLMs) and generative AI workloads.
- Enhance performance tuning using TensorRT/TensorRT-LLM, vLLM, Dynamo, and Triton Inference Server to improve GPU utilization and model efficiency.
- Collaborate with multi-functional teams (engineering, product) and offer technical mentorship to cloud customers implementing AI inference at scale.
- Build custom PoCs for solution that address customer’s critical business needs applying NVIDIA hardware and software technology
- Partner with Sales Account Managers or Developer Relations Managers to identify and secure new business opportunities for NVIDIA products and solutions for ML/DL and other software solutions
- Prepare and deliver technical content to customers including presentations about purpose-built solutions, workshops about NVIDIA products and solutions, etc.
- Conduct regular technical customer meetings for project/product roadmap, feature discussions, and intro to new technologies.
- Establish close technical ties to the customer to facilitate rapid resolution of customer issues.
Requirements
- BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Statistics, Physics, or other Engineering fields or equivalent experience.
- 3+ Years in Solutions Architecture with a proven track record of moving AI inference from POC to production in cloud computing environments including AWS, GCP, or Azure
- 3+ years of hands-on experience with Deep Learning frameworks such as PyTorch and TensorFlow
- Excellent knowledge of the theory and practice of LLM and DL inference
- Strong fundamentals in programming, optimizations, and software design, especially in Python
- Experience with containerization and orchestration technologies like Docker and Kubernetes, monitoring, and observability solutions for AI deployments
- Knowledge of Inference technologies - NVIDIA NIM, TensorRT-LLM, Dynamo, Triton Inference Server, vLLM, etc
- Proficiency in problem-solving and debugging skills in GPU environments
- Excellent presentation, communication and collaboration skills.
Benefits
- Equity
- Benefits
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