Responsibilities:
•   Develop machine learning models and algorithms to address business needs.
•   Collaborate with data scientists and software engineers to design and implement scalable and efficient solutions.
•   Clean, preprocess, and analyze large datasets to extract meaningful insights.
•   Deploy machine learning models into production environments and monitor their performance.
•   Continuously improve model accuracy and performance through experimentation and optimization.
•   Stay up-to-date with the latest advancements in machine learning and related technologies.
•   Communicate findings and results to stakeholders in a clear and concise manner.
Requirements:
•   Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related field.
•   2~5 years of experience in machine learning, data science, or a related field.
•   Proficiency in programming languages such as Python, Java, or Scala.
•   Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn.
•   Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
•   Experience with cloud platforms such as Google Cloud Platform (GCP), including services like BigQuery, Cloud Storage, and AI Platform.
•   GCP Professional Machine Learning Engineer certification is required.
•   Experience with version control systems such as Git.
•   Excellent problem-solving skills and attention to detail.
•   Strong communication and collaboration skills.
Preferred Qualifications:
•   Master's degree or higher in Computer Science, Engineering, Mathematics, or a related field.
•   Experience with distributed computing frameworks such as Apache Spark.
•   Familiarity with containerization and orchestration technologies such as Docker and Kubernetes.
•   Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau.
•   Experience with natural language processing (NLP) or computer vision (CV) techniques.
•   Experience with continuous integration and continuous deployment (CI/CD) pipelines.
•   Contributions to open-source projects or participation in relevant communities.
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