ML Engineer

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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