Own cloud-native MLOps infrastructure for multimodal sensor data ingestion, processing, labeling, validation, model traceability, deployment gates, monitoring, drift detection, and diagnostics. Partner with data scientists, ML engineers, and stakeholders to ensure reliable pipelines, fresh features, robust model performance, and effective post-deployment metrics. Build visualization and support tools that help technical and nontechnical users understand perception and recommendation systems.

Design, build, and maintain infrastructure, internal tools, and platforms supporting computer vision systems deployed on agricultural sprayers. Scale distributed edge systems, connect firmware, machine learning, and user interfaces, manage cloud and bare-metal infrastructure, and test solutions in hardware-equipped environments.

Analyze crop protection and sensor data, develop statistical and machine learning models, build dashboards and recommendation engines, improve perception pipelines, establish data quality measures, and communicate findings across technical and agricultural teams.