Machine Learning Engineer- Fresher

As a Machine Learning Engineer, you will focus on analyzing time series data, particularly vibration and acoustic signals, to develop models for fault detection, diagnosis, and RUL estimation. You will work closely with a multidisciplinary team of data scientists, engineers, and domain experts to design and implement machine learning algorithms that enhance our predictive maintenance capabilities. Key Responsibilities: Analyze and preprocess time series data from vibration and acoustic sensors to develop machine learning models that detect and diagnose faults, estimate remaining useful life, and predict maintenance needs. Collaborate with cross-functional teams to integrate machine learning models into our predictive maintenance platform. Develop and maintain machine learning models using Python and relevant libraries, such as TensorFlow and scikit-learn. Work with data scientists to design and implement experiments to evaluate model performance and identify areas for improvement. Stay up-to-date with the latest advancements in machine learning and predictive maintenance to continuously improve our capabilities. Requirements: Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field. 0-2 years of experience in machine learning or a related field. Strong understanding of machine learning concepts, including supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction. Experience with Python and relevant libraries, such as TensorFlow and scikit-learn. Familiarity with data preprocessing and feature engineering techniques. Ability to work in a fast-paced environment and prioritize tasks effectively. Benefits: Competitive salary and benefits package. Opportunities for professional growth and development. Collaborative and dynamic work environment. Flexible work arrangements, including remote work options. Opportunities to work on cutting-edge projects and technologies. Opportunities to collaborate with a multidisciplinary team of experts.