Careers at Zentrik / 8 open roles

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Reliability & AI Operations Lead

Own the runtime, infrastructure, observability, and AI operations needed for trustworthy customer deployments.

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8
open roles
4
role paths
1
small team

Infrastructure

Zentrik evidence graph with source-backed product context.

Work context

Roles connect to real product systems

Hiring brief

Reliability & AI Operations Lead

Infrastructure, observability, model operations, and eval systems

You bring
Strong judgment and visible work
You own
A clear role with real company scope

Infrastructure / Leadership role

Reliability & AI Operations Lead

Infrastructure, observability, model operations, and eval systems

A senior infrastructure and AI operations role for someone who can make ambitious product workflows dependable.

Own the runtime, infrastructure, observability, and AI operations needed for trustworthy customer deployments.

Start a conversation

A concise note is enough to start.

What you would own

  • Production architecture for runtime, background jobs, integrations, data movement, and customer-facing reliability.
  • Model operations for evaluation, observability, prompt and tool versioning, guardrails, cost control, and data quality.
  • Runbooks, test harnesses, 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.
  • You can move between cloud architecture, DevOps, backend systems, data infrastructure, model operations, and application-level product judgment.
  • You care about evals, reproducibility, observability, incident review, and human approval paths.

Questions you would help answer

  • What infrastructure lets AI-assisted workflows run observably, safely, and cost-effectively?
  • How should Zentrik evaluate and monitor model behavior, job pipelines, data freshness, and reliability?
  • Where should automation remove operational toil without lowering the bar for review?
Zentrik workflow graph showing product context moving into agent work.

What the work feels like

Work here should feel direct, concrete, and high-trust.

We are a small team building a product for product teams. You should expect to work close to customers, code, evidence, product decisions, and the systems that help the company learn faster.

High ownership, small team

You should expect real scope, close collaboration, and enough context to make decisions without waiting for layers of approval.

Artifacts matter

We learn more from repos, prototypes, teardown notes, customer loops, growth systems, eval harnesses, and operating docs than from generic claims.

AI is part of the work

We use AI seriously, but the bar is better judgment, clearer systems, faster learning, and work that can be reviewed.

How we think about fit

Show us how you think and what you have made.

We do not need a polished application package to begin. Strong candidates can point to visible work and explain the choices, tradeoffs, and lessons behind it.

  • 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

Start with a short note and one useful artifact.

Tell us which role you are interested in and why. Include a repo, product teardown, prototype, GTM system, research synthesis, eval harness, shipped product, or short memo if it helps us understand your work.

Contact Zentrik
Tell us which role you want to explore.
  1. 1

    Send a focused note

    Tell us which role fits and include one or two artifacts: a repo, product surface, teardown, GTM system, research synthesis, eval harness, or short memo.

  2. 2

    Talk through your work

    We will talk about the role, 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 an initial domain with clear ownership, expectations, and success criteria.