Machine Learning Operations Engineer

Responsibilities

• Maintain and manage the software configuration of on-premises machine learning hardware to support optimal performance for training neural networks.  
• Set up and maintain cloud-based training environments, primarily on Google Cloud Platform, to facilitate model experimentation and scalability.  
• Automate training workflows to drive continuous improvement of vision models, reducing manual overhead and enhancing efficiency.  
• Develop automated accuracy assessments and generate reports to evaluate and compare the performance of newly trained neural networks against existing models.  
• Ensure predictable and efficient turnaround times for training models with updated datasets to meet project timelines.  
• Organize and manage model weights and associated documentation in various formats for deployment across on-premises, cloud, and edge environments.  
• Apply quantization and pruning techniques to models to enhance computational efficiency without sacrificing accuracy.  
• Design and deploy infrastructure for low-latency inference to enable real-time performance for large-scale models (e.g., vLLMs).

Requirements

• Proven experience with Linux server maintenance, including both on-premises and cloud environments.  
• Proficient in scripting with Bash and Python to streamline system and model management.  
• Hands-on experience with neural network training, data loaders, and data pre-processing pipelines.  
• Familiar with data and model parallelism strategies for improving training speed and efficiency.  
• Knowledgeable in neural network model conversion and optimization for deployment on diverse hardware.

Preferred Qualifications

• Familiarity with Google Cloud Platform for machine learning operations.  
• Experience with specialized hardware platforms such as Nvidia Jetson, Triton Inference Server, and NIM.  
• Skilled in OpenVINO and ONNX for model conversion and optimization.  
• Experience training or fine-tuning large language models (LLMs) would be a significant advantage.  
• Programming experience in Python and C++ is beneficial but not mandatory.  
• Strong written and verbal communication skills for documentation and collaboration.  
• Passion for machine learning technology and an aptitude for problem-solving in fast-paced environments.