KEY RESPONSIBILITIES & ACCOUNTABILITIES
AI System Design and Development
Design, develop, and implement AI solutions to automate and enhance university operations, including service desk automation, administrative task processing, and QA testing systems. Create robust, scalable architectures that integrate with existing university systems and accommodate future growth.
Data Pipeline Development and Management
Design and implement end-to-end data pipelines that efficiently collect, process, and prepare data for AI systems. Build robust ETL processes using tools like Apache Airflow, cloud services, and data warehousing solutions to ensure reliable data flow between source systems and AI applications. Implement data quality checks, monitoring, and governance practices throughout the pipeline.
Machine Learning Implementation and Fine-tuning
Develop and fine-tune machine learning models for specific university use cases, including customizing large language models through prompt engineering, transfer learning, and domain adaptation. Create efficient training pipelines and establish systematic evaluation protocols.
System Integration and Deployment
Integrate AI systems with existing university infrastructure, including identity management, knowledge bases, ticketing systems, and communication platforms. Deploy models to production environments following established MLOPs practices and ensuring appropriate monitoring.
Performance Monitoring and Optimization
Monitor AI system and data pipeline performance, detect and address drift or degradation, optimize resource utilization, and continuously improve model accuracy and efficiency based on real-world usage patterns and feedback.