Careers at Zentrik
8 senior roles
Agent-native brains
All roles

The Reliability Brain

Build the operating layer that makes agentic product workflows observable, evaluable, and dependable across enterprise and mid-market customers.

Read role detailsStart a conversation
Infrastructure
Strategic opening

The Reliability Brain

Cloud infrastructure, model operations, reliability, and eval systems

A CTO, DevOps, infrastructure, and MLOps shape, expressed as one hands-on operator.

Build the operating layer that makes agentic product workflows observable, evaluable, and dependable across enterprise and mid-market customers.

Start a conversation

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

What you would own

  • Production architecture for runtime, background jobs, integrations, data movement, AI workflows, and customer-facing reliability.
  • MLOps patterns for evaluation, observability, prompt and tool versioning, data quality, model behavior, guardrails, and cost control.
  • Infrastructure agents, test harnesses, runbooks, canaries, rollback paths, and automations that reduce toil without hiding risk.

What we would look for

  • You have built reliable SaaS infrastructure for real customers and understand enterprise operational pressure.
  • You can move between cloud architecture, DevOps, backend systems, data infrastructure, model operations, and application-level product judgment.
  • You spend serious time on the system around the system: evals, reproducibility, observability, incident review, and human approval paths.

Questions you would help answer

  • What infrastructure lets agent workflows run observably, safely, and cost-effectively for enterprise and mid-market customers?
  • How should Zentrik evaluate and monitor model behavior, job pipelines, data freshness, and customer-facing reliability?
  • Where should automation remove operational toil without lowering the bar for review?
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.