Overview:
A Machine Learning/Deep Learning Engineer who has experience with scaling model training from one
GPU to multiple GPUs. Must have familiarity with popular Deep Learning Frameworks and distributed
training paradigms to help customers with optimally using their hardware.
Skillset Needed:
– Experience in Generative deep learning modelling concepts
– Understanding of graph theory and exposure to Graph neural networks
– Hands-on experience in implementing Deep Learning based model training and validation
pipelines using either Tensorflow or Pytorch. Pytorch preferred.
– Knowledge of deployment of models in production is a plus
– Experience with Multi-GPU training(Dask ML)
– Understanding of Data Parallelism and Model Parallelism
– Understanding of algorithmic and engineering challenges, benefits, bottlenecks and trade-offs
when scaling to larger number of GPUs
– Familiarity with Docker containers
– Strong hands-on experience coding with Python
– ML and SQL knowledge
– Experience with Dataframes and Arrays on GPUs
Ways to stand out from the crowd:
– API Building using frameworks such as Flask or web development
– Spark 3.0
– C , CUDA & Cython
– R, Matlab knowledge
– Experience with Multi-Node Training
– Inference on large clusters – Triton
– Knowledge of cluster management and orchestration, Kubernetes