Senior Solutions Architect, Financial Services Capital Markets

NVIDIA NVIDIA · Semiconductors · NY +2 · Remote

NVIDIA is seeking a Senior Solutions Architect for Financial Services Capital Markets to work with clients on High-Performance Computing and AI workloads. The role involves performing proof-of-concepts, optimizing ML/DL models on GPU architectures, and building collateral for finance industry use cases. Requires strong Python/C++ coding, experience with ML frameworks, and deploying models at scale.

What you'd actually do

  1. Partner with NVIDIA Engineering, Product, and Sales teams to secure design wins at customers. Enable development and growth of NVIDIA product features through customer feedback and proof-of-concept evaluations.
  2. Perform proof-of-concepts working side by side with clients, engineers, and other architects on in-depth analysis, profiling and optimization of machine learning/deep learning models to ensure the best performance on current- and next-generation GPU architectures.
  3. Work directly with client ML researchers and developers/engineers on business-impacting workflows, projects, and issues to drive success using NVIDIA technology.
  4. Facilitate rapid resolution of customer issues and promote the highest levels of customer satisfaction.
  5. Build collateral (notebooks/ blogs) applied to Finance industry use-cases such as ML/DL, recommender systems, GNN, monte-carlo simulations, Quantitative Finance, etc. by working closely with customers.

Skills

Required

  • BS/MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or other Engineering fields (or equivalent experience)
  • 12+ years experience as an ML/Software Engineer
  • Python
  • C++
  • ML/DL algorithms
  • PyTorch
  • Spark
  • Dask
  • Jax
  • TensorFlow
  • deploying ML/DL models at scale
  • public cloud computing
  • on-prem HPC clusters

Nice to have

  • C/C++ programming proficiency
  • software design
  • programming techniques
  • algorithms
  • performance optimizations
  • NVIDIA GPU architectures
  • NVIDIA CUDA-x libraries
  • cuBLAS
  • cuDNN
  • MLOps technologies
  • containers
  • data center deployments
  • cluster management software
  • enterprise developers
  • HPC applications
  • data analytics applications

What the JD emphasized

  • proven track record in writing code in Python, C++
  • Experience with ML/DL algorithms with frameworks such as PyTorch, Spark, Dask, Jax, TensorFlow
  • Skilled in deploying ML/DL models at scale on public cloud computing and/or on-prem HPC clusters in production

Other signals

  • customer feedback
  • proof-of-concept evaluations
  • ML/DL models
  • GPU architectures
  • MLOps technologies