Yêu cầu công việc
If you can do everything listed above, you’ve got what it takes. Perhaps some of the following would be helpful too:
Strong hands- on experience in Machine Learning algorithms & libraries like Tensorflow, Pytorch, Keras, Scikit, Pandas.
Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our machine learning products.
Experience of at least 3 years in AI, Machine Learning, Data Science roles.
Solid knowledge of Machine Learning fundamental i.e. knowledge of model’s variant and use cases, end- to- end ML lifecycle process, model development, model selection, hyperparameter optimization, feature engineering, evaluation.
Design, develop and maintain scalable and resilient AI/ML products, e.g. Recommendations system & Personalization, Chatbot, Search, Forecasting, etc. You are responsible for end- to- end machine learning product life cycle.
Good verbal and written communication in English is required.
Design, implement, and optimize the core modules of our MLOps platform. This includes developing solutions that enhance our machine learning workflows’ scalability, efficiency, and robustness.
Proven track record of operating independently, stakeholder management, demonstrating creativity, and delivering results.
Monitoring the performance of the MLOps platform and proactively identifying areas for optimization. This includes analyzing system performance, ensuring low latency, and optimizing cloud resource consumption for cost- effective operations.
Experience of at least 3 years in Software Development
Strong technical hands- on experience in Python.
Hands- on experience with cloud providers such as Google Cloud Platform (GCP) or Amazon Web Services (AWS) or Microsoft Azure. Your familiarity with cloud services and infrastructure will be essential in architecting scalable and reliable MLOps solutions.
Practical experience of ML modeling, involving the entire process from conceptualizing ideas to developing, evaluating, and deploying models, with the added advantage of having expertise in monitoring and managing model performance after deployment.
