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Machine Learning Engineer
4 months ago
Develop and deploy machine learning models and algorithms to solve complex business problems and optimize processes.Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand requirements, gather data, and define project goals.
Ensure you read the information regarding this opportunity thoroughly before making an application.
Conduct exploratory data analysis, preprocess and clean data, and perform feature engineering to extract relevant information for model training.Select appropriate machine learning algorithms and techniques, and develop models that are accurate, robust, and scalable.Train and fine-tune machine learning models using large-scale datasets, and optimize model performance through techniques such as hyperparameter tuning.
Evaluate and validate models using appropriate metrics and statistical methods, and iteratively refine models based on feedback and insights.Deploy machine learning models into production environments, ensuring scalability, reliability, and efficiency.Collaborate with software engineers to integrate machine learning models into software systems and develop APIs for model inference.
Continuously monitor and evaluate model performance and implement necessary updates or improvements.Stay up to date with the latest advancements in machine learning algorithms, techniques, and tools, and apply them to solve real-world problems
Requirements:
Bachelor's or higher degree in computer science, data science, or a related field. Advanced degrees or relevant certifications are advantageous.Strong programming skills in languages such as Python, R, or Java, and experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Solid understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning.Experience with data preprocessing, feature engineering, and data visualization techniques.Proficiency in working with large-scale datasets, SQL and NoSQL databases, and big data processing frameworks (e.g., Hadoop, Spark).Familiarity with software engineering best practices, including version control, testing, and code review.Strong mathematical and statistical skills, with the ability to apply statistical methods and evaluate model performance.
Excellent problem-solving and analytical thinking, with the ability to understand complex business problems and develop innovative solutions.Effective communication skills to collaborate with cross-functional teams and present findings and insights to both technical and non-technical stakeholders.
Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and knowledge of deploying and managing machine learning models in cloud environments is a plus.This job description provides a general overview of the responsibilities and qualifications expected from a Machine Learning Engineer.
The specific requirements and responsibilities may vary depending on the organization and the nature of the projects.
