The AI Engineer designs AI solutions for data management and governance, employing machine learning to enhance data quality and compliance.
Job Purpose and Impact
The AI Engineer will 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
Minimum requirement of 2 years of relevant work experience. Typically reflects 3 years or more of relevant experience.
The AI Engineer will 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 moderately complex data from different data sources, analyses the ways in which datasets may be biased and applies mitigation strategies.
- DATA ANALYSIS: Reviews moderately complex data sets for exploratory data analysis to identify trends and patterns that inform business strategies across various areas.
- MODEL DEVELOPMENT: Implements and deploys artificial intelligence models, including review of ongoing performance to solve moderately complex business problems and derive actionable insights.
- AI ENGINEERING: Applies 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: Collaborates documenting development and code in ways that allow for support and knowledge sharing.
- COMMUNICATION: Communicates techniques and results to technical and non-technical audiences.
- CONTINUOUS LEARNING: Supports examination of existing and emerging artificial intelligence and optimization principles, theories, and techniques to deploy artificial intelligence and optimization models into production, improving the organization's analytical capabilities.
- STAKEHOLDER MANAGEMENT: Works closely with businesses to understand needs, and supports collaboration with cross functional teams to implement artificial intelligence models for digital applications.
Qualifications
Minimum requirement of 2 years of relevant work experience. Typically reflects 3 years or more of relevant experience.
Top Skills
Automation
Data Governance
Data Management
Generative Ai
Machine Learning
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What you need to know about the Chennai Tech Scene
To locals, it's no secret that South India is leading the charge in big data infrastructure. While the environmental impact of data centers has long been a concern, emerging hubs like Chennai are favored by companies seeking ready access to renewable energy resources, which provide more sustainable and cost-effective solutions. As a result, Chennai, along with neighboring Bengaluru and Hyderabad, is poised for significant growth, with a projected 65 percent increase in data center capacity over the next decade.
