The role

The engineering team is five developers deep, building fast. The CTO knows industrial operations from the inside and is good at the kind of problem-solving that has no existing playbook. What is not there yet is serious academic depth: the kind that knows when a problem needs a knowledge graph versus a relational table, reaches for the right optimisation model, and can reason about why a time series forecast is drifting without being walked through it. That is what this seat is for.

The CTO brings the operational intuition. You bring the depth. You will not be expected to have walked a plant or to know how a procurement approval process works inside a large manufacturer. You will be expected to understand ontologies from first principles, to have the modelling range that lets you tackle a scheduling problem one week and a forecasting challenge the next, and to know the stack — Go, Python, Google Cloud — well enough to own it.

The inputs Oppr processes are as varied as the plants it serves. ERP data from SAP and similar systems. SCADA feeds. Data historians with tens of millions of rows of timestamped process values. Spreadsheets, images, voice, knowledge bases. Each of these is a different world with different data quality realities. You will own how we connect to all of them and how we get reliable, structured data out the other side.

You will also shape how the team codes. We are building an AI-first engineering practice — using tools like Claude seriously, not as a shortcut. You will set the patterns, review the output, and hold the bar. The five developers on the team code fast; your job is to make sure what they ship is also right.

What you will do

• Own the solution stack end to end. Know every layer — the Go backend, the Python data and AI work, the cloud infrastructure, the integrations — and make sure it holds together as a coherent, maintainable whole.
• Solve the hard modelling problems. Resource optimisation, scheduling under constraints, time series forecasting, machine learning on real industrial signals. Bring the academic toolkit and use it on problems that actually matter.
• Build and own the integrations. Connect Oppr to the real industrial world: ERP systems, SCADA, data historians at scale, spreadsheets, images, voice. Each integration is a small product in itself, not a one-off script.
• Establish the AI-first coding practice. Set the approach, the review patterns, and the quality standard for AI-assisted development across the team. Own what comes out of those sessions.
• Keep the knowledge graph and asset ontology sharp. The graph is at the core of how Oppr models industrial reality. You are responsible for the depth, the correctness, and the design decisions behind how it grows.

What you bring

• Academic depth. MSc, PhD, or a track record that makes the equivalent clear. We have people who build fast; we need someone who also thinks rigorously, and who reaches for the right algorithm rather than the first one that works.
• Knowledge graphs and ontologies from first principles. Not a tutorial familiarity — the kind that lets you design a schema from scratch, argue the trade-offs, and know when a graph is the wrong model entirely.
• Modelling range. Time series forecasting, optimisation, linear programming, machine learning, scheduling under resource constraints. You do not need to be a specialist in all of them, but you need to know which tool fits which problem and be able to implement it.
• Full-stack capability across Go and Python. Python for data and AI is your natural home. Go does not need to be your first language, but you need to be able to read, extend, and take ownership of a Go backend.
• AI-first as a craft. You use Claude and similar tools extensively and thoughtfully. You know where they make mistakes. You can set the review discipline that keeps AI-assisted code reliable at team scale.
• You are near The Hague and in the office three to four days a week. The hard problems get solved in the room.
• Bonus: exposure to industrial data systems. Data historians, SCADA, ERP integrations, or time series at industrial scale. You do not need it on day one — but the curiosity to go deep on how a plant actually generates data is what we will look for.

What you are measured on

The depth and reliability of what gets built. Hard problems solved correctly. Integrations that hold under real industrial data. A knowledge graph that models customer reality accurately. And a team that ships AI-assisted code you would genuinely sign off on.

How this role grows

This is a senior individual contributor seat with a clear ceiling above it. As the engineering team grows and the product matures, there is a natural path toward Technical Lead: setting architecture direction, owning the quality standard across the team, and shaping how engineering at Oppr is done. It is a seat we intend to fill from within.