Infor

The Infor Decision Analytics and Science (IDEAS) team is responsible for setting the innovation strategy at Infor. As an MLOps engineer, you will contribute to the development of highly automated, robust, reusable, explainable, and self-serving AI solutions that are rapidly deployed and managed at scale. You will rely on a combination of analytics and technical expertise to turn the data vision into reality. MLOps engineers work closely with the solution architect to finalize a solution blueprint and own the implementation of the reference architecture across all our solutions. The MLOps engineer role isn’t about the technical implementation only. It also involves working with the data science and business teams to build data models that are faithful to the business and that make building AI solutions easier.

A Day in The Life Typically Includes:

·       Design, develop, and maintain scalable data management features in the Augmented Intelligence (AI) suite using Infor Platform Technology.

·       Design, develop, and maintain reference architectures that integrate AI solutions with other Infor and third-party solutions.

·       Design, optimize, and implement data models and semantic business layers for AI solutions to reflect business objects, requirements, and constraints.

·       Build reusable, scalable, and reliable data pipelines for AI solutions.

·       Define and implement metrics that provide visibility into data quality. 

·       Write unit, integration, and performance tests and implement a CI/CD pipeline.

·       Maintain development artifacts and technical documentation in GitLab.

Required skills:

·       Experience in building and orchestrating big data pipelines of structured and unstructured data sets.

·       Experience in developing and integrating data-centric analytics solutions.

·       Proficiency in developing software with Python and SQL; software version control with Gitlab.

·       Experience with cloud-based server monitoring and data workflows.

·       Understanding of ETL and data modeling; exposure to distributed system architecture.

·       Understanding of basic ML models and algorithms for Time Series Forecasting, Regression, Classification, Clustering, Recommendation Systems, and Anomaly detection.

Preferred Qualifications:

·       Exposure to MLOps tools.

·       Experience with large-scale data processing (e.g. PySpark).

·       Experience in data analysis and visualization (e.g. Tableau, Power BI).

·       Familiarity with task management (e.g. JIRA).