What it's about.

octonomy automates complex knowledge work at an expert level,

implemented in under 20 days, GDPR-compliant, Made in Germany. As a Forward-Deployed Engineer (m/f/d), you'll be responsible for multiple enterprise implementations simultaneously, each end-to-end. You'll be on-site with the client, understand their systems, data, and processes, model the domain, build the components and data pathways yourself, and bring the result into stable production operation. Management, stakeholders, and deadlines are all part of the job: forward-deployed means that the technology and the client relationship are in the same hands.

Where the projects are actually decided:

at the data level. The quality of an implementation depends less on the final polish than on the underlying domain model: What entities exist, how are they related, which system is the true state of affairs for which field, what happens with duplicates and conflicting data, and what questions can even be answered with this data set? You clarify things early, write them down, and make the decision – even when there's no one at the client's end to decide for you. Anyone who sees data work as a preliminary step to the actual project will find this role demanding.

Here's what multiple responsibilities specifically mean:

You manage several implementations in parallel, and they're never at the same stage. While one is going into acceptance testing, you're negotiating the scope in the next project, and in the one after that, you're building the connection to a system that no one here has even touched before. You decide for yourself what needs attention today, where a risk arises before it actually materializes, and when to say no to the client. No one assigns you tasks, and no one checks whether you've actually started them. No one verifies

what you've delivered in 12 months.

• Managed multiple enterprise implementations in parallel and brought them into stable production operation – data model, architecture, implementation, acceptance.
• A domain model that has withstood productive operation and served as a starting point for the next customer in the same industry.
• At least one component that someone else reused in the next project without asking.
• A recurring customer requirement that you have moved from a single project into the product roadmap.
• A technical architectural decision that the space initially opposed but which has proven to be correct.

Who this is for:

People who think in systems and data – and deliver results themselves.

You model your clients' domains before you build anything: entities, relationships, taxonomies, metadata. Graph-based modeling is familiar to you, not just as a concept. You've built data flows from heterogeneous source systems – document repositories, wikis, CMS, product databases, file shares, ERP, and CRM – including incremental synchronization, deduplication, mapping, and everything else that makes unstructured content analyzable. You can assess whether a dataset is reliable before it's even made.

You've managed end-to-end enterprise integrations, from API design (REST and GraphQL) to system integration, and you write the code yourself, to a quality level suitable for production. You can juggle multiple projects simultaneously without letting any of them stall. You have practical experience with LLMs, prompt engineering, and enterprise AI deployments. You use AI as a lever in your own work and can concretely describe how your output has changed as a result. And you present what you've built to the client – ​​from the developer level to the C-suite. Fluent English is a must.

This isn't for you if:

you want to simply work through a finished delivery template (we don't have a

fixed standard yet; you'll help build it); you prefer to delegate the building process –

this role isn't just about coordination; you want to outsource data modeling and data preparation; or you expect external dictation of your priorities. If someone has to tell you what to work on today, you won't be happy here. You'll learn

what's truly necessary and what isn't.

Native-level German (C2) is relevant for directly advising German clients. We don't check academic degrees or specific graduation dates. Nor should a specific programming language be on your resume – we are interested in what you have built, whether it went into operation, how much of it you decided without support, and how you dealt with data that was not as promised.

How we work. Flexible models, remote or hybrid. Flat hierarchies, clear decisions. You deploy independently; no one stands between you and the production environment. Freshly funded with one of the largest seed rounds in Europe (USD 20 million).

Let's build the future of work – together. Seriously. 

At octonomy, you can expect an environment that values ​​quality, fosters creativity, and delivers real, measurable results. If you're keen to bring top-level AI into everyday business and scale with us, we look forward to getting to know you. 

• octonomy.ai | We grow output. Not headcount.