Just the other day, a job posting flashed across my LinkedIn feed, one that reeked of ambition and the scent of wet concrete. **Rement** is looking for nothing less than a **VP or Head of Engineering**. A person who will become the CTO’s right hand and lead the engineering department into a bright, circular future.
**A few words about the company.** Rement is a bold climate-tech startup from Karlsruhe. These guys have figured out how to transform construction waste (old concrete) and carbon dioxide into new, high-quality, and eco-friendly building materials. Their mission isn’t just to reduce harm, but to close the loop in the construction industry. It sounds like science fiction turned reality.
**What’s their pain point?** Judging by the job description, Rement is at the most dangerous and exciting juncture in any deep-tech project’s development. They have a working technology under lab/pilot conditions. Now they need to make a giant leap – to build a first-of-a-kind (FOAK) commercial plant. This is a transition from the cozy R&D sandbox to the harsh industrial battlefield. The risks are colossal. The cost of error is millions of euros and, potentially, the fate of the entire company. They need a “superbrain” that can hold hundreds of variables in mind: from chemical processes and equipment specifications to regulatory acts (EN/DIN), contractor management, and future team building. They are looking for a one-man band.
**But what if this orchestra could be digital?** What if, instead of one, albeit brilliant, human brain, susceptible to stress, burnout, and cognitive biases, this task were handled by an AI-powered system? Sounds provocative, doesn’t it? Let’s imagine.
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— Alex, have you seen the Rement job posting? They’re looking for an engineering director for their first plant. Eight years of experience, leadership skills, a track record in scaling… Classic. They’ll find some gray-haired guru from Siemens, and he’ll build everything for them in three years, following last century’s blueprints.
— Mike, don’t start. Who do you think will build their plant? A neural network, perhaps? Will it draw them blueprints in Midjourney? This is real production, not a SaaS app. It requires experience, intuition, and the ability to negotiate with contractors.
— I’m not talking about a single neural network. I’m talking about a system. Let’s call it the **”AI VP of Engineering.”** It’s not one tool, but an integrated platform that would take on 80% of the functions of that very VP. Here’s how it could work.
**1. Design and Scale-up (Scale-up & Deployment)**
Instead of a person spending weeks consolidating lab data into a process flow diagram, we create a **Digital Twin** of the entire production process. An AI model, trained on R&D data, runs thousands of simulations to find the optimal plant design.
* **Tools:** Platforms like **AspenTech** or **Siemens Process Simulate**, enhanced with machine learning models, select the ideal parameters for each component: from the concrete crusher to the carbonization chamber.
* **Concrete step:** The AI analyzes a database of industrial equipment and generates not just a specification, but several plant configuration options with calculated CAPEX/OPEX, delivery times, and even risks from each supplier. The human (CTO) only needs to choose the most suitable option.
* **Reducing distrust:** We start small. We task the AI with designing a single conveyor belt. We compare its solution with that of an experienced engineer. When the AI proposes a 15% more energy-efficient and 10% cheaper option, trust will begin to grow.
**2. Engineering Leadership and Strategy (Engineering Leadership)**
— But who will build the team, Alex? Who will define the strategy?
— Strategy is, first and foremost, data analysis. The AI platform analyzes the building materials market, energy prices, logistics chains, and proposes several development scenarios to the CTO. For example: “Given the current CO₂ price dynamics, it’s more profitable to build a second plant in the Rotterdam port area in 3 years, rather than in Munich.”
As for the team, the AI won’t conduct interviews. But it will analyze the project plan and say: “In Q2 2026, we will need two process engineers with knowledge of DIN 1045 standards and experience with carbon capture systems.” And it will immediately generate a draft job description for HR.
**3. Translating R&D into Reality and Working with Partners**
This is the AI’s strongest suit. It can find non-obvious correlations in laboratory data that the human eye misses. “Look, if we slightly change the granulometric composition of the raw material, the yield of finished product will increase by 2.7%, which is equivalent to €300,000 in additional annual profit.”
For working with partners and regulations, an **LLM fine-tuned on technical documentation** is used.
* **Tools:** A solution based on **Palantir Foundry** for integrating all data (from R&D to finance) and a custom LLM, trained on all EN/DIN standards, patents, and scientific articles in materials science.
* **Concrete step:** Before submitting a project for approval, it is “fed” to an AI auditor, who cross-references each component with thousands of pages of regulatory documents and issues a report: “Attention, component 5.3.1 does not comply with EN 206 fire safety requirements. Recommended solution: …”. This saves months of approvals.
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### How to check that our “AI VP” hasn’t gone rogue?
Validation is a key question. And there’s no magic here.
1. **Expert Review:** The results of the AI’s work (drawings, specifications, strategic recommendations) are sent for audit to independent engineering companies. Exactly as one would check the work of a hired VP.
2. **End-to-End Simulation:** A project created by one AI system is tested in a completely different, independent simulation environment. We look for discrepancies and weaknesses.
3. **Incremental Implementation:** The riskiest or most innovative solutions are first implemented on a smaller-scale pilot plant.
Ultimately, the final decision always rests with humans – with the CTO and CEO. But the difference is that they make it, not just with the opinion of one, albeit very experienced, specialist, but with the results of an analysis of millions of options, free from human biases.
Rement stands on the threshold of great achievements. And perhaps, to build the factory of the future, they don’t just need a person from the past, but a partner from the future – an intelligent, trainable, and impartial engineering system. And the funds saved on the VP’s salary and options could be invested in another R&D project. How’s that for a provocation?
Источник: https://www.linkedin.com/jobs/view/4280905714/
