Machine Learning Engineer

Company Overview

Company information is currently not specified.

Job Summary

The Machine Learning Engineer will be responsible for developing and maintaining end-to-end machine learning solutions. This role involves overseeing the entire lifecycle of machine learning projects, from data ingestion to model training and deployment. The successful candidate will ensure that the machine learning systems effectively meet business objectives and enhance operational efficiency.

Responsibilities

• Develop and maintain end-to-end machine learning solutions, covering data ingestion, model training, and serving.
• Build and optimize data pipelines for data preprocessing, feature engineering, and model consumption for both batch and real-time scenarios.
• Design and implement machine learning training pipelines, including experiment tracking, hyperparameter tuning, and model versioning.
• Set up and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for data and ML workflows.
• Develop responsive frontend interfaces using React.js to enable real-time interaction with machine learning services, including dashboards, visualizations, and input forms.

Qualifications

• Strong understanding of machine learning concepts and experience with various algorithms.
• Proficiency in programming languages such as Python and Java.
• Experience in building and optimizing data pipelines and managing CI/CD processes.
• Familiarity with React.js for frontend development.
• Knowledge of cloud platforms (e.g., AWS, Google Cloud) and orchestration tools.
• Ability to work in a collaborative environment and communicate technical concepts to non-technical stakeholders.
• Degree in a related field such as Computer Science, Data Science, or Engineering (BSc or higher preferred).

Preferred Skills

• Experience with big data technologies such as Apache Spark or Hadoop.
• Proficiency in using machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.
• Knowledge of advanced machine learning techniques (e.g., deep learning, reinforcement learning).
• Familiarity with Docker and Kubernetes for containerization and orchestration.

Experience

Minimum experience requirements are currently not specified.

Environment

Details on the typical work setting and location are currently not specified.

Salary

Salary information is currently not specified.

Growth Opportunities

Advancement opportunities in machine learning and data science are available, fostering career development within the organization. Specific paths for progression have not been detailed.

Benefits

Details on offered benefits are currently not specified.