1. Hands-on AI (Artificial Intelligence) Solution Development & Deployment:
- Computer Vision / AI@AOI (Automated Optical Inspection).
- Machine Learning, predictive models & anomaly/deviation detection.
- LLM (Large Language Model)/ Generative AI applications.
- Develop PoC → validate → industrialize → deploy into production.
- Ensure model performance, robustness and maintainability
2. AI Strategy, Roadmap & Governance:
- Define and maintain HcP (Hochiminh city Plant) AI strategy & multi-year roadmap.
- Establish AI governance: use-case intake, prioritization, data ownership, model risk, ethics, security & compliance.
- Translate business challenges into AI opportunities.
- Define standards for AI architecture, model lifecycle, MLOps, documentation and operational handover.
3. Business Value & Use-case Management:
- Identify and assess AI opportunities together with business functions.
- Prioritize based on business value, feasibility and data readiness.
- Define measurable KPIs/business cases.
- Track realized benefits after implementation.
4. AI Capability & Ecosystem:
- Build AI competence within HcP.
- Coach project teams and enable business functions to adopt AI.
- Collaborate with central/PS (Power Solution) AI experts and external partners.
- Reuse and scale proven solutions instead of developing isolated solutions
5. AI Architecture & Technology:
- Define appropriate AI architecture considering on-premise, edge and hybrid deployment.
- Integration with manufacturing/IT systems and data platforms.
- Evaluate technologies/frameworks and support build-vs-buy decisions.
- Ensure scalability, security and lifecycle management.
6. Other tasks assigned by manager