Primary Roles & Responsibilities:

Understanding of machine learning principles and algorithms, specifically LLMs such as GPT-4, BERT, etc
Extensive knowledge of deep learning frameworks like TensorFlow, PyTorch, or Keras, which are used to train LLMs.
Deep knowledge of computer architecture, particularly with regard to GPUs. Understanding how to maximize GPU utilization and efficiency.
Familiarity with cloud platforms (AWS, Azure, GCP) and experience in managing and optimizing resources for training deep learning models.
Experience with containerization and orchestration tools like Docker and Kubernetes.
Understanding of parallel and distributed computing principles. Experience with distributed training of deep learning models can be beneficial.
Familiarity with distributed computing and big data technologies like Hadoop and Spark.
Familiarity with machine learning operations (MLOps) principles and tools for managing the ML lifecycle

Secondary Responsibility :

Should have worked on the infrastructure side of multiple machine learning projects, ideally involving deep learning models such as BERT and Transformers.
Experience in managing resources and optimizing performance for large-scale machine learning tasks.
Experience in managing and optimizing GPU resources, both on-premise and in the cloud, for machine learning tasks.
Experience in handling the challenges associated with training large models, such as managing memory usage, optimizing data loading, and troubleshooting hardware issues.
Ideally, should have worked closely with data scientists and machine learning engineers to understand their infrastructure needs and provide efficient solutions

Key Success Metrics:

Ensure timely deliverables. Spot Training Infrastructure fixes. Lead technical aspects of the projects. Error free deliverables.

Senior Machine Learning Engineer