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Role Purpose / ObjectiveWe are looking for an AEC Prototyping Engineer to reinforce our Innovation Team  with strong digital capabilities, high technical adaptability and a hands-on prototyping mindset. The role is designed for someone who can understand engineering problems, explore the right technologies, build working POCs quickly, demonstrate them to users and improve them based on feedback.

The AEC Prototyping Engineer transforms architectural, structural and civil engineering ideas into rapid, testable product prototypes, validates them with customers and works with R&D to turn successful concepts into scalable Graitec capabilities.

This is a customer-facing, cross-functional innovation role rather than a narrow AI-agent development position. You will work closely with Product, R&D, Support, Customer Success, Services and selected customers to convert promising ideas into tangible, validated workflows.

What success looks like after 6 months:

• Several AEC use cases have been converted into demonstrable prototypes or POCs across relevant Graitec products and workflows.
• Prototype assumptions have been tested with internal experts and selected customers (market fit), with clear feedback captured and translated into concrete improvements.
• Successful concepts have been documented and handed over to Product team and R&D with enough clarity to support prioritization, industrialization or further experimentation.
• A repeatable rapid-prototyping approach has been established: idea intake, problem framing, build, customer validation, iteration and handover.

This role is hands-on and cross-functional, working closely with R&D, Product, Support, Customer Success and Marketing to turn real needs into validated engineering workflows and product concepts.

Key Responsibilities

Engineering prototyping & workflow innovation 

• Translate AEC uses cases into rapid, working prototypes using the most appropriate tools and technologies.

• Build testable workflows around CAD/BIM, architecture, structural analysis, design automation, project data, documentation and engineering productivity use cases.

• Use AI-assisted development, Python, APIs, low-code/no-code tooling, scripting or existing product capabilities to accelerate experimentation.

• Convert incomplete or exploratory ideas into clear problem statements, testable assumptions and practical POC scopes.

• Move quickly between topics and product areas while maintaining a strong focus on AEC market value and customer needs.

Customer validation & iteration

• Demonstrate prototypes to internal experts, R&D, users and selected customers in a clear and practical way (based on business use cases)
• Collect qualitative feedback, observe user behaviour and identify where the prototype creates value or fails to address the need.
• Change direction rapidly based on feedback and improve the prototype through short iteration cycles.
• Help Product teams convert validation insights into backlog items, product opportunities or go/no-go recommendations.

Technology exploration & AI as an accelerator

• Explore emerging technologies, including AI, automation and agentic patterns, when they are relevant to solving an engineering problem.
• Apply practical AI capabilities such as prompt-based workflows, AI-assisted coding, product assistants, RAG-enabled knowledge access, APIs or AI skills when they accelerate prototyping.
• Avoid technology-first solutions: select tools based on the problem, user need, feasibility and expected business impact.
• Collaborate with R&D and IT on secure, maintainable and scalable approaches when a prototype shows potential for productization.

Cross-functional collaboration & handover

• Work with the Solutions lines, R&D, marketing to frame and validate ideas.
• Produce concise documentation explaining the engineering problem, prototype scope, assumptions tested, feedback received, limitations and recommended next steps.
• Support internal enablement by sharing lessons learned, prototype demos and reusable patterns with relevant teams.
• Support incubation steps for selected ideas 

• Degree in Architecture, Structural Engineering, Civil Engineering, or a related discipline, combined with a practical understanding of engineering workflows.
• Professional experience delivering projects within an Architecture, Engineering, or Construction (AEC) environment.
• Experience with CAD/BIM workflows, structural analysis, design automation or AEC project data.
• Hands-on experience with Python scripting, APIs, data formats, automation tools, or product extensibility frameworks.
• Experience in rapid prototyping, hackathons, innovation projects, internal tools, student projects or customer facing demos.
• Experience working with Product, R&D or customer-facing teams in an agile environment.

• Strong digital capabilities and hands-on interest in software, automation and AI.
• High technical adaptability and learning agility: able to understand unfamiliar technologies, experiment with them hands-on and apply them to new engineering problems without extensive onboarding.
• Ability to rapidly prototype solutions using AI-assisted development, Python, APIs, low-code platforms, and existing product capabilities.
• Comfortable presenting proof-of-concepts (POCs), gathering user feedback, and translating insights into solution improvements.
• Ability to translate ambiguous concepts into testable workflows and iterate rapidly based on feedback and results.
• Good communication skills to work directly with customers and internal teams.
• Fluent in English.
• Familiarity with AI concepts such as LLMs, RAG, AI assistants, tool use, prompting or agentic workflows.
• Interest in real-world enterprise constraints: security, maintainability, performance, traceability and scalability.