Graz, Austria · Hybrid Full-time Customer Engineering Start: flexible
You will work directly with engineering customers to turn fragmented enterprise data into usable context graphs, workflows, and AI-powered applications.
Where engineering reality meets the context layer. The Forward Deployed Engineer sits between customers, product, and engineering. The role turns real engineering data problems into deployed Context64 solutions.
You’ll be the person in the room when a Tier‑1 OEM opens their PLM, CAD, ERP and simulation systems and asks what’s possible. You’ll write the entity model that fits their domain, configure the Data Context Hub to match their governance constraints, and ship the application surfaces engineers actually use day‑to‑day.
Inside the company, you’re the source of truth for what real engineering teams need next — the feedback loop from production back into platform direction.
A week in the role.
Work with customers to understand engineering data landscapes — sources, ownership, governance constraints, current pain.
Configure DCH models, workflows, and graph structures that fit each domain — automotive, manufacturing, energy, pharma.
Build application surfaces on top of context graphs: explorers, builder UIs, embedded agent interfaces.
Collaborate with product and engineering teams to ship platform features the next customer will need too.
Support pilots from discovery to production — onboarding, acceptance, governance sign‑off.
Translate customer requirements into product feedback that ends up on the roadmap, not in a deck.
What makes someone great in this role.
Strong engineering and systems thinking. You see the structure under the surface and can describe it.
Production experience with React, TypeScript, Python, APIs, or data systems — at least two of those, in real environments.
Ability to understand complex enterprise data environments quickly enough to be useful in the first month.
Clear communication with technical and non‑technical stakeholders. You write, you draw, you don’t hide behind acronyms.
Ownership mindset. You see a gap, you close it — without waiting for permission or process.
Comfort working in early‑stage product environments where some of the road is still being paved.
Doesn’t disqualify you if missing — but helps.
Knowledge graphs, semantic data, or Neo4j experience.
Direct work with PLM, ALM, ERP, CAD, or requirements systems.
AI agents, LLM workflows, retrieval architectures.
Automotive, aerospace, industrial, or systems engineering background.
German language skills (B2+) — most customers operate in EU/DACH.
What this role actually offers.
Build infrastructure, not demos
Production systems running inside the customer perimeter — engineering AI that stays useful after the launch deck closes.
Work close to customers
The product team sits one channel away. Your customer signal moves the roadmap inside the same week.
Shape a young platform
DCH and M4AI are still defining their primitives. The structural calls you make now compound for years.
Solve hard context problems
Graph design, governance, retrieval, and agent reasoning over engineering ground truth — at real scale.
What we disclose. Austria · Statutory disclosure
For this position, the minimum gross annual salary will be disclosed according to Austrian legal requirements. Actual compensation depends on experience, qualifications, and role scope — and is typically meaningfully above the legal minimum for senior candidates. Equity participation is included.
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