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Experience:

5 years required

Skills:

Architecture, Teamwork, SketchUp, English

Job type:

Full-time

• Design 3D visuals for booths, exhibitions, and event spaces.
• Create layouts and structures that match the vibe of each campaign.
• Visit spaces and check setups when needed.
• Work closely with the production team to turn your ideas into real builds.
• What you will need?.
• Bachelor's degree in design, architecture, or anything related.
• At least 3-5 years of hands-on experience in events, exhibitions, or similar work.
• Proficiency in 3D Max, SketchUp, Vray, Corona, Photoshop, and Illustrator.
• Knows how to work with materials, building techniques, and event setups.
• Full of ideas and can build on creative briefs from the team.
• Enjoys teamwork, stays sharp, and always takes responsibility.
• Always ready to learn and grow.
• Can communicate in English is a plus.
• What will you get?.
• Flexible working hours.
• Snack bar and ice cream.
• Massage service.
• Cars & Motorcycle parking allowance (50% support by the company).
• Medical allowance and Dental allowance.
• Annual health check.
• Mental health service.
• Annual salary adjustment & bonus (based on performance).
• Company outing.
• Annual leave days (up to 15 days).
• Special leave day (marriage, anniversary).
• Work Location: Thanapoom Tower Near BTS Nana, MRT Petchaburi.
• Dare to cross the Whiteline!.

Skills:

Architecture

Job type:

Full-time

• Design computational data models, reusable schemas, and structured data frameworks that improve interoperability, consistency, and machine usability across the R&D data ecosystem.
• Define reusable entity structures, metadata patterns, relationships, and data contracts that support integration across experimentation systems, analytics environments, and digital products.
• Translate scientific and business complexity into scalable model logic and reusable ...
• Context- and Knowledge-Driven Data Modelling.
• Develop context-rich data models that connect scientific data, metadata, documents, protocols, business rules, and domain concepts into reusable knowledge assets.
• Create information structures that preserve scientific meaning and operational context to improve consistency across functions and over time.
• Improve discoverability and reuse by formalizing relationships, definitions, and contextual attributes across fragmented systems and data sources.
• Ontology, Knowledge Graph, and RAG Foundations.
• Apply ontology principles to define consistent concepts, hierarchies, relationships, and machine-readable rules across priority R&D data domains.
• Support the development of R&D knowledge graph foundations by modeling relationships between experiments, protocols, observations, methods, assets, and decisions.
• Enable Retrieval-Augmented Generation (RAG) and other knowledge-driven AI approaches by improving retrieval structures, contextual linkages, and connections between structured and unstructured information.
• AI-Ready Data Platform Enablement.
• Contribute to AI-ready data platforms by defining reusable knowledge layers, integration patterns, and data-readiness standards.
• Partner with platform owners, Bioinformatics leads and technical stakeholders to scalable AI integration, and reliable information retrieval.
• Collaboration with Data Scientists and Bioinformatics Leads.
• Collaborate with Data Scientists and Bioinformatics lead to ensure analytical, digital, and AI solutions are built on reusable and scalable data foundations.
• Contribute to shared architecture discussions, design reviews, and foundational modelling decisions aligned with business and platform needs.
• Documentation, Standards, and Change Enablement.
• Document modelling standards, ontologies, schemas, and reusable reference patterns to support consistent adoption across teams.
• Provide technical guidance on computational data models, ontology structures and AI-ready data design approaches.
• Governance, Safety, and Professional Standards.
• Ensure data models and knowledge structures align with governance, security, lineage awareness, traceability, and responsible AI enablement standards.
• Balance architectural rigor, usability, innovation, usability, and practical business value to support scalable implementation.