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Job Description –
Google Cloud Platform (GCP) Applied ML Engineer: An expert engineer with an eye for AI. You want to change how the world works and lives by taking AI out of the lab and into everyday life. You are primarily a software engineer with a very strong interest in data science (not the other way around i.e. this is not a data scientits role). As a machine learning engineer, you’ll be required to develop a more holistic understanding of the software you are building, including transferring some of the software engineering priciples to the data science world. Having a deep understanding of the mathematical underpinnings of the Machine Learning algorithms is a big plus, but it’s NOT necessary. However, you’ll be required to know what algorithms are available —or when and how to apply them. Machine Learning output is actually working software!
Role & Responsibilities–
• "Use deep learning and machine learning to solve business problems
• Run deep learning and machine learning projects from beginning to end
• Work on projects that cover everything from business understanding to model building, validation, and deployment
• Use Accenture tools and techniques
• Report on your progress regularly in writing and face-to-face
• Stay up-to-date on new products that clients could use
• Tweak existing methods and procedures to create solutions to moderately complex problems
• Use your judgment to craft solutions to complex problems or seek guidance as needed
• Prototype and demonstrate products for clients in customer environments.
• Work with peers and senior colleagues in Accenture and at client companies
• Help the sales team identify and classify opportunities
• Find out where clients need help and produce “as is” and “to be” scenarios
• Implement management’s strategy"
• Integrate massive datasets from multiple data sources for data modelling
• Implement methods for automation of all parts of the predictive pipeline to minimize labour in development and production
• Formulate business problems as technical data problems while ensuring key business drivers are captured in collaboration with product management
• Knowledge in machine learning algorithms especially in recommender systems
• Extracting, Loading, Transforming, cleaning, and validating dat
• Designing pipelines and architectures for data processing
• Creating and maintaining machine learning and statistical models
• Querying datasets, visualizing query results and creating reports
• Extensive travel may be required
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