Machine Learning Engineer
Summary:
We are seeking a talented and motivated Machine Learning Engineer to join our team. The ideal candidate will have at least 3 years of experience in the field, with a strong background in machine learning, data science, and software engineering. You will be responsible for designing, developing, and deploying machine learning models to solve complex problems and enhance our products and services.
Key Responsibilities:
• Design, develop, and implement machine learning models and algorithms.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Perform data preprocessing, feature engineering, and model validation.
• Optimize and scale machine learning models for production.
• Monitor and maintain deployed models, ensuring their performance and accuracy over time.
• Conduct experiments to evaluate model performance and identify areas for improvement.
• Stay up-to-date with the latest developments in machine learning and artificial intelligence.
• Document processes, models, and methodologies for reproducibility and knowledge sharing.
Qualifications:
• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.
• At least 3 years of experience as a Machine Learning Engineer or in a similar role.
• Proficiency in programming languages such as Python, R, or Java.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong understanding of statistical analysis and data mining techniques.
• Familiarity with data preprocessing, feature engineering, and model evaluation.
• Experience with big data technologies (e.g., Hadoop, Spark) is a plus.
• Knowledge of cloud platforms (e.g., AWS, Azure, GCP) for deploying machine learning models.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Preferred Skills:
• Experience with MLFlow for experiment tracking and model management.
• Proficiency in using PySpark for big data processing.
• Strong SQL skills for database querying and manipulation.
• Knowledge of model serving techniques and tools for deploying machine learning models at scale.
• Familiarity with DevOps practices and tools for CI/CD in machine learning.
• Understanding of software development best practices, including version control and testing.
