Intern for Information Technology, PETRONAS in Kuala Lumpur, WP Kuala Lumpur
This position is no longer accepting applications(closed Aug 17, 2026).
Summary
Build and maintain an AI agent that generates cost estimates for engineering projects using historical data, material take-offs, and project strategies, then deliver automated reports and dashboards for stakeholders.
- Curate and develop an AI Agent to generate cost estimates based on engineering deliverables, material take-offs (MTO), construction scope, and project execution strategies.
- Collect, consolidate, and maintain historical cost estimates, benchmarking databases, market intelligence, and estimating tools.
- Define AI taxonomy, cost breakdown structures (CBS), work breakdown structures (WBS), and data classification standards.
- Design and develop a centralized database structure to manage engineering, quantity, cost, productivity, and benchmarking data.
- Perform data sanitization, cleansing, standardization, transformation, and normalization to ensure data quality and consistency.
- Analyze historical project data to identify cost trends, productivity benchmarks, cost drivers, and estimating patterns.
- Develop AI prompts and domain-specific reasoning frameworks to support accurate and explainable cost estimation.
- Implement validation mechanisms using historical benchmarks, cost rules, and industry standards to improve estimate accuracy and reliability.
- Establish confidence scoring, audit trails, and traceability for AI-generated estimates.
- Create an interactive AI Agent capable of providing personalized, role-based responses and recommendations.
- Generate automated outputs including:
- Executive summaries
- Cost estimation reports
- Benchmarking analyses
- Cost variance reports
- Risk and contingency assessments
- Management dashboards and insights
- Enable conversational querying and decision-support capabilities for estimators, project controls, engineering teams, and project management.
- Continuously improve AI performance through user feedback, historical project outcomes, and model refinement.
- Ensure governance, data security, and compliance requirements are incorporated into the AI solution.