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The Product Systems Brain

Build product surfaces and agent workflows where customer evidence, product intent, and implementation context stay connected.

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Product engineering
Innovative role

The Product Systems Brain

Full-stack product systems, AI workflows, and eval-backed shipping

A product engineer who can use agents as a real delivery surface, not a demo layer.

Build product surfaces and agent workflows where customer evidence, product intent, and implementation context stay connected.

Start a conversation

A useful first note shows how you think and what you have made.

What you would own

  • Full-stack product work across public surfaces, core workflows, integrations, and agent handoffs.
  • Agent harnesses, prompt and tool contracts, eval paths, and review loops that make AI-assisted product work reliable.
  • Fast shipping with enough product judgment to avoid output that does not create value.

What we would look for

  • You have shipped real software and can show the systems you use to work faster with AI.
  • You care about UX, data shape, reliability, and maintainability, not only code volume.
  • You can turn ambiguous product intent into a working, reviewable, production-minded artifact.

Questions you would help answer

  • How should agents receive enough context to build the right thing?
  • Which product workflows need deterministic software and which can use probabilistic help?
  • How should evals become part of everyday product engineering?
What the work feels like

The product is the operating layer for better product decisions.

The role is not to add AI decoration to old workflows. The role is to preserve product intent from customer evidence to shipped work, while making every loop more inspectable, more reliable, and easier for a human team to trust.

Product intent stays human

AI can accelerate the work, but the reason to build still comes from evidence, taste, tradeoffs, and accountability.

Visible systems beat claims

We look for artifacts: GitHub repos, workbenches, eval notes, customer loops, market systems, prototypes, and operating docs.

The harness matters

Great AI-native work depends on context, evals, guardrails, review loops, and proof that the next cycle gets sharper.

Zentrik agent workflow graph showing product context moving into agent work.
How we think about fit

Range matters, but judgment matters more.

We are more interested in visible systems of work than in claims of AI fluency. Strong candidates can show how they create leverage without confusing output volume for customer value.

GitHub or public work artifacts

Agent harness or eval loop

Product or business judgment

Market or customer learning system

Infrastructure or MLOps system

Community or enablement motion

Personal workbench or knowledge system

Hiring process

Show us the system behind the work.

A strong note could include a GitHub repo, product teardown, prototype, GTM system, research synthesis, eval harness, personal workbench, shipped product, or a short memo about where you think Zentrik should go.

Contact Zentrik
Tell us which role fits your strongest proof.
  1. 1

    Send a proof artifact

    Share a repo, product teardown, prototype, GTM system, research synthesis, eval harness, customer system, or short memo.

  2. 2

    Work through the role shape

    We will talk about the domain, the level of ownership, the systems you have built, and where your judgment is strongest.

  3. 3

    Review a real operating problem

    The useful signal is how you structure ambiguity, design feedback loops, and decide what is worth doing next.

  4. 4

    Define the first scope

    If there is a fit, we will shape the initial domain clearly enough that you can start creating leverage quickly.