Digital Agriculture & AI Integration Intern

About the Role

Vylor is seeking a motivated Digital Agriculture & AI Integration Intern to support strategic digital-transformation initiatives across global seed production and agricultural operations. This internship offers hands-on experience in project coordination, stakeholder engagement, data quality, responsible AI enablement, and digital-tool validation within a global agricultural business.

Duration: Six months preferred; qualified candidates available for a three-month summer internship may also be considered.

Key Responsibilities

Digital Project Coordination and Feasibility

• Support project scoping, requirements gathering, and documentation for regional digitization efforts
• Assist in evaluating the use and effectiveness of current digital tools, such as SCiO moisture sensors, and help assess their potential scalability
• Coordinate with regional field teams, global support teams, and approved technology partners to clarify integration pathways and internal-system requirements
• Track actions, risks, decisions, and project progress, and communicate updates to key stakeholders

AI Knowledge Preparation and Validation

• Collect, organize, and curate approved standard operating procedures, training materials, and product information for potential use by internal large language model (LLM) and AI tools
• Support the development and testing of prompt-and-response approaches for internal Copilot and AI use cases
• Evaluate AI-generated responses for accuracy, relevance, consistency, and alignment with approved source materials
• Document gaps, errors, and improvement opportunities while following Vylor's data-governance, privacy, information-security, and responsible-AI requirements

Field-Tool Validation and Insight Enablement

• Collaborate with agronomy, field, and data teams to evaluate insights from internal digital platforms, remote sensing, and weather and soil data
• Support field teams in assessing irrigation insights and validating digital monitoring methods against field observations
• Help improve dashboards and reporting by identifying missing data, inconsistencies, outliers, and opportunities to improve usability
• Summarize findings and communicate practical recommendations to technical and nontechnical stakeholders

Qualifications

Required

• Currently pursuing a bachelor's degree in agricultural engineering, data science, agronomy, computer science, information systems, agricultural systems technology, or a related field
• Minimum 3.0 cumulative GPA
• Strong interest in digital agriculture, artificial intelligence, data integration, or agricultural technology
• Strong written and verbal communication skills, with the ability to work across technical and nontechnical teams
• Demonstrated organization, attention to detail, critical thinking, and problem-solving skills
• Ability to handle company information responsibly and follow established data, privacy, and information-security requirements

Preferred

• Experience with Microsoft Excel, Power BI, or similar data-analysis and visualization tools
• Basic scripting or data-analysis experience using Python, R, SQL, or a comparable language
• Exposure to agricultural field operations, remote sensing, geographic information systems (GIS), irrigation, or environmental data
• Familiarity with generative AI, prompt development, data curation, or evaluating AI-generated outputs

What You Will Gain

• Exposure to global digital-agriculture and seed-production strategies
• Practical experience supporting responsible AI use cases, knowledge preparation, and data governance
• Understanding of field operations, irrigation strategies, agronomic data, and digital monitoring tools
• Experience coordinating projects and communicating with regional and global stakeholders
• Opportunities to strengthen data-analysis, digital-tool validation, and professional communication skills