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.

Originally posted on Himalayas