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.
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.
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.