Machine Learning Ops Engineer

Duties And Responsibilities

• Package ML models in containers, i.e. Docker, and deploy to production environments.
• Design and implement ML pipelines for data ingestion, training, evaluation, and deployment.
• Setup and maintain model monitoring and logging of deployed models to track performance metrics like accuracy, latency, and resource utilization.
• Collaborate with a diverse team including data scientists to transition models from research to production, software engineers to integrate ML models into broader application architectures, and system engineers to maximize hardware resources (cpu, fpga, gpu) to optimize performance.

Job Qualifications

• Typically requires a bachelors degree in computer science, engineering, mathematics, or a related technical discipline from an accredited institution.
• May substitute equivalent machine learning engineer experience in lieu of education.
• Strong proficiency in Python.
• Experience with other languages like C++ is also valuable.
• Understanding of machine learning principles and frameworks like PyTorch (preferred), TensorFlow, etc.
• Practical experience with Docker for deployment and packaging applications.
• Experience with optimizers such as TensorRT, onnx, and openVio.
• Proficient with Linux command line environment.
• Able to obtain and maintain DoD Security Clearance is required.