全职 AI Value Engineering Intern 工作, 薪水, Univers 公司招聘中 - Ricebowl

AI Value Engineering Intern

Location: Singapore

Team: AI Value Engineering

Openings: 3

Duration: Minimum 3 months (3–5 days/week; full-time preferred)

Start Date: As soon as possible

About Univers

Univers is the platform for Physical AI, enabling the world’s critical operations to perceive, understand, and act in real time.

Our platform connects physical assets, enterprise systems, and live operational data with deep industrial intelligence and domain-specific AI agents. It helps organisations move beyond monitoring and fragmented AI experiments to orchestrate autonomous operations, while keeping people in control at every critical decision point.

Univers manages more than 1,005 GW of energy assets with AI, connects over 400 million sensors and devices, and serves more than 800 enterprise customers across 45 countries. Across energy and utilities, transportation and logistics, built environments, and industrial manufacturing, we help customers improve uptime, efficiency, resilience, and speed.

For more information, please visit *************

About the Role

Are you interested in how AI can solve complex business challenges and create measurable real-world impact?

As an AI Value Engineering Intern, you will work with the AI Value Engineering team to partner clients on their AI transformation journeys. You will help understand their most important business priorities, identify the AI opportunities that can create the greatest value, and work with business, data, product, and engineering teams to turn those opportunities into real solutions.

This is a hands-on opportunity to experience the full journey of AI value creation—from analysing a client’s business model, P&L, processes, and KPIs, to shaping business cases, prioritising use cases, supporting solution delivery, and helping translate ideas into measurable outcomes. You will gain exposure to client and leadership discussions while working under the guidance of experienced team members.

Projects may span transport and logistics, manufacturing, healthcare, retail, energy, and other asset-intensive sectors, with use cases across AI-orchestrated energy transition, operational improvement, and commercial growth.

Key Responsibilities

Client Problem Solving & Value Identification

• Support client discovery through industry research, business analysis, meeting preparation, and participation in client discussions and workshops.
• Break down client business models, P&Ls, operating processes, and KPIs to identify value pools, pain points, and improvement opportunities.
• Build structured problem statements, value-driver trees, financial models, and quantitative business cases with guidance from the team.
• Analyse client and market data to size opportunities, test hypotheses, and support evidence-based recommendations.

AI Opportunity Prioritisation & Solution Shaping

• Translate business needs into clearly defined AI use cases, success metrics, and prioritised opportunity roadmaps.
• Work closely with AI data, AI systems, product, engineering, and domain teams to assess data availability, technical feasibility, delivery requirements, and expected business impact.
• Support development of client-ready proposals, executive presentations, workshop materials, and value-sharing models.
• Research emerging AI technologies, industry practices, competitors, and academic literature to inform solution design and recommendations.

Project Delivery & Cross-functional Coordination

• Support project planning and execution across pilots, proofs of value, and contracted AI transformation programmes.
• Coordinate actions, dependencies, decisions, and deliverables across client stakeholders and internal business, data, product, and engineering teams.
• Help define delivery milestones and success measures, track progress, and surface issues requiring resolution.
• Contribute to reusable AI Value Engineering methodologies, industry value-driver libraries, use-case databases, analytical models, and project templates.
• Use modern AI and analytics tools to improve the speed, depth, and quality of research, analysis, modelling, and content development.

Qualifications

• Currently pursuing a Bachelor’s, Master’s, or Ph.D. degree in Engineering, Computer Science, Data Science, Business Analytics, Economics, Operations Research, Finance, or another quantitatively rigorous discipline.
• Strong analytical and structured problem-solving ability, with the potential to break complex questions into clear analyses and actionable outputs.
• Comfortable working with quantitative information and learning to build financial models, analyse datasets, and connect business KPIs to value creation.
• Proficiency in modern AI tools for research, analysis, modelling, and content development is a strong advantage; strong working proficiency in Excel and PowerPoint is required, while exposure to SQL, Python, Power BI, Tableau, or similar analytics tools is a plus.
• Able to communicate clearly within a team, absorb coaching, and translate analysis into well-structured written materials and presentations.
• Curious, diligent, detail-oriented, and willing to go deep into unfamiliar business, technical, and industry topics.
• Relevant experience through consulting, strategy, analytics, product, technology, research, case competitions, hackathons, or previous internships is a plus, but not required.
• Strong written and spoken English. Fluency in written and spoken Mandarin is an advantage, but not required.

What You’ll Gain

• First-hand exposure to how enterprises identify, prioritise, and implement high-value AI opportunities.
• Experience analysing real business challenges and connecting strategy, data, technology, and financial impact.
• Opportunities to participate in client and leadership discussions and learn how executive-level decisions are shaped.
• Hands-on collaboration with AI data, AI systems, product, engineering, and domain experts across the full transformation lifecycle.
• Practical experience producing consulting-quality analyses, financial models, executive presentations, and project deliverables.
• A strong foundation for future careers in management consulting, AI strategy, value engineering, product management, business analytics, or technology transformation.