Machine Learning Engineer

  Understanding of data structures, data modeling and software architecture. Deep knowledge of Math, Probability, Statistics and Algorithms. Experience with machine learning platforms such as Microsoft Azure, Google Cloud, IBM Watson and Amazon. Big Data Environment: Hadoop, Spark. Programming Languages: Python, R, PySpark. Supervised Unsupervised Machine Learning: Linear Regression, Logistic Regression, K-means Clustering, Ensemble Models, Random Forest, SVM, Gradient Boosting. Sampling Data: Bagging Boosting, Bootstrapping. Experience of machine learning Algorithms and Libraries.     Roles Responsibilities   Work with Data Scientists and Business Analysts to frame problems in a business context. Assist all the processes from data Collection, Cleaning and Preprocessing to training models and deploying them to production. Understand Business Objectives and Developing models that help to achieve them, along with metrics to track their progress. Explore and visualize data to gain an understanding of it, then identify differences in data distribution that could affect performance when deploying the model in the real world. Define validation Strategies, Preprocess or Feature engineering to be done on a given Dataset and Data augmentation pipelines. Analyze the errors of the model and design strategies to overcome them. Collaborate with data engineers to build data and model pipelines. Manage the Infrastructure and Data pipelines needed to bring code to production and demonstrate end-to-end understanding of applications (Including, but not limited to, the machine learning algorithms) being created.   Role: Data Engineer Industry Type: IT Services & Consulting Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: Software Development Education UG: Any Graduate PG: Any Postgraduate Key Skills IT servicesProduct engineeringData modelingAnalyticalMachine learningData structuresVulnerabilityBusiness solutionsInformation technologyPython