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.