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The Senior AI Engineer designs AI solutions for data management, enhances data governance capabilities, and collaborates with teams to improve data quality and analytics.
Job Purpose and Impact
The Senior AI Engineer will lead the design and build AI-enabled solutions that enhance Data Management and Data Governance capabilities across Cargill. In this role, you will apply machine learning, generative AI, and automation to improve data quality, metadata management, master data, data classification, lineage, and governance workflows-helping ensure data is trusted, compliant, and business-ready. You will collaborate closely with Data Engineering, Data Domain & Governance, Architecture, Security, and Business/Function teams to embed intelligence into core data platforms and processes.
Key Accountabilities
Qualifications
The Senior AI Engineer will lead the design and build AI-enabled solutions that enhance Data Management and Data Governance capabilities across Cargill. In this role, you will apply machine learning, generative AI, and automation to improve data quality, metadata management, master data, data classification, lineage, and governance workflows-helping ensure data is trusted, compliant, and business-ready. You will collaborate closely with Data Engineering, Data Domain & Governance, Architecture, Security, and Business/Function teams to embed intelligence into core data platforms and processes.
Key Accountabilities
- DATA PREPARATION MANAGEMENT: Conducts extraction and integration of complex data from different data sources, analyses the ways in which datasets may be biased and applies mitigation strategies.
- DATA ANALYSIS: Reviews complex data sets for exploratory data analysis to identify trends and patterns that inform business strategies across various areas.
- MODEL DEVELOPMENT: Develops and deploys artificial intelligence models, including monitoring and evaluating ongoing performance to solve complex business problems and derive actionable insights.
- AI ENGINEERING: Establishes software and artificial intelligence engineering patterns and principles to design, develop, test, integrate, maintain and troubleshoot complex and varied generative artificial intelligence software solutions and incorporates security practices in newly developed and maintained applications. .
- DOCUMENT & REPORTING: Documents development and code in ways that allow for support and knowledge sharing.
- COMMUNICATION: Presents techniques and results to technical and non-technical audiences.
- CONTINUOUS LEARNING: Examines existing and emerging artificial intelligence and optimization principles, theories, and techniques to develop and deploy artificial intelligence models into production, improving the organization's analytical capabilities.
- STAKEHOLDER MANAGEMENT: Works closely with businesses to understand needs, and collaborates with cross functional teams to develop artificial intelligence models for digital applications.
Qualifications
- Minimum requirement of 4 years of relevant work experience. Typically reflects 5 years or more of relevant experience.
Top Skills
Data Governance
Data Management
Generative Ai
Machine Learning
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