AI Engineer at Procter & Gamble Company

Warsaw, Masovian Voivodeship, Poland

Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Consumer GoodsIndustries

Demonstrated skills in applied Big Data technologies
Python language for manipulating data
SQL, Linux Shell Scripting
Experience in Agile, CI/CD and DevOps methodologies – knowledge of using appropriate tools – e.g. Jira, GitHub Actions, Sonarqube
Create (design/code) advanced ELT and analytic applications operating on medium/high volume of data
Design/build database/data warehouse solutions to support BI processes
Cloud services frameworks, Azure and/or GCP
Strong written and verbal English communication skills to influence others
Demonstrated use of data and BI tools
Ability to handle multiple priorities
Ability to work collaboratively across functions and organize work
Nice to have
Practical experience in AI/ML technologies and practices, including MLOps
Conversant knowledge of emerging data and AI technologies and trends
Build ML/AI solutions to support the Data Engineering and operations team in self-healing systems, outage prevention, or data quality
Familiarity with containerization technology and tools (e.g., Docker, Kubernetes)
Big Data technologies (Hive, Impala) and NoSQL databases

Partners with data scientists, data managers, analysts, infrastructure engineers, and peer AI Engineers to develop, operationalize, integrate, and scale new algorithmic products
Engages in proof-of-concepts and experiments to evaluate new models and technologies
Develops high-quality, standard-compliant, and tested code
Refactors the code of others as necessary
Contributes code and tools to central re-usable libraries and repositories
Develops data models, model features, data quality tests, ETL/ELT pipelines, and distributed compute architectures
Deploys algorithmic products across the clouds, skillfully leveraging cloud-native services
Manages and automates cloud resources (IaC)
Implements DevOps, DataOps and MLOps principles in day-to-day work
Develops continuous integration, delivery, and training pipelines
Leverages observability and automation tools to manage operations of algorithmic products across the stack