Machine Learning Engineer (MLE)  

A Machine Learning Engineer trains, validates and evaluates machine learning and deep learning models which perform predictive tasks in various domains such as computer vision, natural language understanding and time-series analysis. Some examples include visual recognition models, activity detection using sensor data and demand forecasting.

• Translate and refine business goals into appropriate machine learning objectives.
• Design and implement ML/DL solutions and integrate them with various Big Data platforms and architectures.
• Create and maintain ML pipelines that are scalable, robust, and ready for production.
• Collaborate with domain experts, software developers, and data scientists.
• Troubleshoot ML/DL model issues, including recommendations for retrain, re-validate, and improvements/optimization.

Requirements:

• Hands-on experience in building ML models deployed into real-world business applications or research.
• Working knowledge of ML/DL algorithms (classification, regression, clustering, hyperparameter tuning, etc).
• Proficiency with Python and libraries for machine learning such as scikit-learn and pandas.
• Good understanding of Deep learning frameworks such as Tensorflow, Keras, PyTorch, MXNet, etc.
• Experience in using computer vision libraries such as OpenCV, PIL.

Preferred:

• Experience working with cloud services platform (AWS or GCP) to build ML/DL pipelines.
• Experience in multi-GPU model training with CUDA.
• Experience in ML experiment tracking tools (e.g. WandB, Neptune, TensorBoard).
• Experience in model deployment using Docker (e.g. AWS SageMaker, Google Kubernetes Engine).
• Experience in model compression or quantization for on-edge-device inference.
• Experience with Continuous Integration and Continuous Delivery(CI/CD).
• Relevant certifications in machine learning and cloud technologies (e.g., AWS, Coursera) would be a plus.