Machine Learning Engineer - Remote

Overview

As a Machine Learning Engineer at Brainlabs, you will be a key member of our Data Science team, responsible for developing and deploying machine learning models to solve complex business challenges. You will work with large datasets, build scalable data pipelines, and collaborate with cross-functional teams to deliver impactful solutions.

In Short

• Develop and maintain web scraping infrastructure to extract data from various online sources.
• Apply data cleaning, transformation, and feature engineering techniques to prepare datasets for model training.
• Design, develop, and deploy machine learning models (e.g., regression, classification, clustering) to address business challenges.
• Optimize model performance and accuracy while staying informed about new developments in AI/ML.
• Assist in deploying models into production and monitor their performance.
• Identify potential issues and contribute to solutions for optimal model operation.
• Collaborate with engineering and DevOps teams for smooth integration and deployment.
• Work effectively with cross-functional teams to understand requirements and deliver valuable solutions.
• Communicate technical concepts clearly to both technical and non-technical stakeholders.
• Contribute to technical documentation and knowledge sharing.

Requirements

• A Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• 2-5 years of experience in developing and deploying machine learning models.
• A strong understanding of machine learning algorithms and data structures.
• Proficiency in Python and experience with relevant libraries (e.g., pandas, NumPy, scikit-learn).
• Practical experience with web scraping techniques and tools.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) is a plus.
• Excellent communication and collaboration skills.

Benefits

• Efficient development and deployment of machine learning models with measurable business impact.
• Smooth and scalable data pipeline implementation.
• Continuous monitoring and optimization of model performance.
• Effective collaboration with teams across departments.
• Clear and insightful communication of technical concepts.