Ml Engineer-Advanced Analytics

Your role and responsibilities

As an ML Engineer with Advanced Analytics skills, you will apply your strong understanding of machine learning techniques and their applications to design, develop, and deploy production-ready ML software. You will utilize popular libraries such as scikit-learn, TensorFlow, or PyTorch to ensure that ML solutions are efficient, scalable, and maintainable.

Your primary responsibilities will include:

Design and Develop ML Software: Apply various ML algorithms, including regression, classification, clustering, and recommender systems, to create production-ready ML software.

Deploy and Maintain ML Solutions: Ensure that ML solutions are efficient, scalable, and maintainable, and deploy them to meet business needs.

Apply ML Techniques: Utilize machine learning techniques to solve complex problems and improve system performance.

Develop Distributed Systems: Design and develop distributed systems and ML components to support large-scale ML applications.

Implement ML Algorithms: Implement ML algorithms using popular libraries such as scikit-learn, TensorFlow, or PyTorch to drive business outcomes.

Required education

Bachelor's Degree

Preferred education

Master's Degree

Required technical and professional expertise

Machine Learning Techniques: Exposure to applying various ML algorithms, including regression, classification, clustering, and recommender systems, to solve complex problems and improve system performance.

ML Software Development: Experience working with popular libraries such as scikit-learn, TensorFlow, or PyTorch to design, develop, and deploy production-ready ML software.

Distributed Systems: Exposure to designing and developing distributed systems and ML components to support large-scale ML applications.

ML Solution Deployment: Experience working with deploying and maintaining ML solutions to ensure they are efficient, scalable, and maintainable.

Algorithm Implementation: Exposure to implementing ML algorithms using popular libraries to drive business outcomes.

Preferred technical and professional experience

Popular Library Proficiency: Exposure to utilizing popular libraries such as scikit-learn, TensorFlow, or PyTorch for designing, developing, and deploying production-ready ML software.

Distributed System Knowledge: Exposure to designing and developing distributed systems and ML components to support large-scale ML applications.

ML Algorithm Implementation: Exposure to implementing ML algorithms using popular libraries to drive business outcomes.

Years of Experience:
10 to 17