AI Engineer
External
Procter & Gamble · Warsaw Downtown OfficePrepare for this interview
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Job Location WARSAW DOWNTOWN OFFICE Job Description We are looking for a passionate AI Engineer who is eager to make an impact on their career and take on a new challenge! With us, you will Collaborate with cross-functional teams: Partner with data scientists, data managers, analysts, infrastructure engineers, and peer AI Engineers to develop, integrate, and scale new algorithmic products Evaluate new models and technology: Engage in proof of concepts and experiments to assess the efficiency of new models and technologies Ensure code quality: Develop high-quality, standard-compliant, and tested code, and refactor code as necessary Contribute to code and tool libraries: Contribute code and tools to central re-usable libraries and repositories for enhanced efficiency and collaboration Develop data models and pipelines: Design data models, model features, data quality tests, ETL/ELT pipelines, and distributed compute architectures Deploy algorithmic products: Deploy algorithmic products across cloud platforms, leveraging cloud-native services effectively Manage and automate cloud resources: Implement Infrastructure as Code (IaC) to manage and automate cloud resources efficiently Implement best practices: Apply DevOps, DataOps, and MLOps principles in day-to-day work, including developing continuous integration, delivery, and training pipelines Ensure operational efficiency: Utilize observability and automation tools to manage operations of algorithmic products across the technology stack Contribute to P&G's ecosystem: Participate in strategic efforts to build and maintain P&G's ecosystem of reusable code components, and contribute code to open-source projects following P&G IT policies Job Qualifications Experience with Python Familiarity with AI and ML concepts and frameworks Experience in data engineering, data science, machine learning operations Proven understanding of designing efficient, robust, and automated pipelines for big data Strong background in CI/CD pipelines (such as GitHub Actions ) Familiarity in operating on, and deploying to a cloud environment (such as Azure)
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