Middle/Senior Machine Learning Engineer

• Working and professionally communicating with the customer’s team
• Taking up responsibility for delivering major solution features
• Participating in requirements gathering & clarification process, proposing optimal architecture strategies, lead the data architecture implementation
• Developing core modules and functions, designing scalable and cost-effective solutions
• Performing code reviews, writing unit, and integration tests
• Scaling the distributed system and infrastructure to the next level
• Helping client’s research team with implementation, training, testing, and tuning of deep learning models
• Developing AWS machine learning infrastructure to support and maintain model serving and training
• Providing guidance and best practices to effectively and securely leverage Amazon SageMaker service for interaction with a model
• Creating reusable project templates to create the infrastructure for MLOps solutions for CI/CD of ML models
• Collaborating with a research team to identify best-fit models and open-source alternatives related to language and signal processing to obtain the best possible result

Requirements

• Strong Python and SQL knowledge and experience
• Experience with PySpark and Airflow
• Experience with model implementation and tuning using popular Machine Learning frameworks like PyTorch, Keras, TensorFlow
• Ability to understand machine learning development and deployment processes
• Solid experience with SageMaker and all the features it provides (like Sagemaker Pipelines)
• Ability to create and adjust SageMaker pipelines for the end-to-end ML development cycle
• Experience in working with Deep learning models for processing
• At least an Upper-Intermediate level of English