Main Purpose:

• The AI Solutions Engineer is responsible for the design, development, deployment, and operationalization of AI and machine learning models to solve strategic and operational challenges across the university. This includes working on projects in student success, human resources, finance, procurement, and other institutional domains.
• The incumbent will lead AI development efforts (70%), including data pre-processing, model training, validation, and full-cycle deployment into production environments (APIs, dashboards, workflow tools). The remaining 30% involves supporting reporting and analytics functions to translate insights into institutional decision-making.

Brief Description        
Key Responsibilities:

• Design and train machine learning models, perform feature engineering and model selection, develop reproducible and modular machine learning pipelines, integrate models into real-time and batch processing architectures, deploy models to production environments, monitor and evaluate model performance post-deployment, package models for stakeholder-facing platforms, and document the full model lifecycle.
• Design, implement, and maintain CI/CD pipelines, automate model training workflows, implement robust version control, establish audit trails, and automate logging systems 
• Transform complex machine learning outputs and statistical findings.
• Ensure compliance with institutional and national data protection policies, particularly POPIA, apply fairness and bias-mitigation techniques, evaluate and document model explainability using tools such as SHAP, LIME, develop and maintain ethical impact assessments (EIAs), promote responsible AI education and awareness.
• Ensure comprehensive, relevant, and accurate operational, management, and statutory reports are available,  support ad-hoc reporting and exploratory analysis requests
• Engage stakeholders across multiple institutional domains, facilitate co-design sessions and discovery workshops, act as a liaison between technical teams and business units, document solution requirements, and support stakeholder capacity-building.
• Exemplify and promote the BIS core values in the BIS Service Charter.

Requirements:

• Qualification: Master’s in Data Science, Computer Science, Information Engineering, Mathematical Statistics, or equivalent
• Experience: Minimum of 3 years in AI/ML engineering, data science, or analytics roles

Essential Skills:

• Proficient in Python, SQL, and model development libraries (scikit-learn, XGBoost, pandas)
• Experience deploying models using Flask, FastAPI, or similar
• Familiar with Docker, Git, MLflow, Airflow, or other MLOps tools
• Strong grasp of model evaluation, bias detection, and interpretability
• Solid understanding of enterprise architecture, APIs, and data security standards
• Experience in programming PL/SQL, SQL, Python, Oracle Analytics Cloud (OAC),  SAP Business Objects, and PowerBI
• Experience in data visualization and communication of results to diverse audiences
• Contribute to the creation and maintenance of dashboards, reports, and forecasts for internal users and external stakeholders
• High level of analytics, interpersonal, verbal, and written communication skills.
• Utilize statistical techniques for data analysis and provide insights into university processes and performance
• Participate in the development of data standards and procedures within the analytics team
• Collaborate with senior analysts to identify and prioritize analytical projects that support decision-making processes
• Training and Support to end-users on how to access and utilize reports and dashboards and develop and maintain documentation on reports, documentation and data processes.

Preferences:

• Understanding of University Academic Structure and systems
• Understanding of University data.

Deadline:30th November,2025