How to Hire LLMOps Engineers: Running LLM Systems in Production

Building an LLM application is a project. Running a portfolio of them is a platform problem: which model each call uses, what happens when a provider fails, how a prompt change is tested and rolled out, what everything costs by feature, and who responds when quality drops at two in the morning. LLMOps engineers own that platform. This guide covers what they do, how to test for the skills, and how to engage them, drawing on FISTA Solutions' AI enablement practice. The readiness standard they enforce is in the LLM production readiness whitepaper and the observability practice in the AI observability whitepaper.

What does an LLMOps engineer do?

An LLMOps engineer builds and operates the shared platform for LLM applications and agents: the gateway that routes, falls back, caches, and meters; tracing and quality monitoring; evaluation pipelines integrated into CI as release gates; prompt, model, and configuration versioning; shadow and canary deployment with rollback; cost attribution and budgets; and incident response for availability, quality, and cost. Gateway design is in what is an ai gateway and deployment stages in what is a canary deployment.

How does LLMOps differ from MLOps?

Concern	MLOps	LLMOps
Model source	Trained in-house	Hosted or open models used via prompts
Core pipeline	Training, features, registry, serving	Gateway, prompts, retrieval, evaluation, deployment
Versioning	Model and feature versions	Prompt, model, retrieval, and tool configurations
Quality signal	Accuracy on labeled data, drift	Graded outputs, groundedness, safety, drift
Cost driver	Training and serving compute	Tokens, context size, routing
Failure modes	Data drift, serving errors	Provider changes, injection, quality regressions, cost spikes

Organizations with both custom models and LLM applications often combine the teams. The MLOps side is in hire mlops engineers.

What skills should you test for?

Platform and infrastructure engineering; API gateway design and operation; observability with tracing and sampling; CI/CD and release engineering; evaluation pipeline integration and gate design; configuration and version management for prompts and models; cost engineering; incident management; and enough understanding of LLM behavior to diagnose quality regressions. Evaluation integration is in how to build a ci cd pipeline for machine learning and cost engineering in how to build an ai cost dashboard.

What interview scenario predicts performance?

Describe an incident: a provider silently updated a model, and sampled quality on one feature dropped while error rates stayed flat. Ask how they would detect it, confirm it, mitigate it, and prevent recurrence. Strong candidates describe quality sampling and alerts, evaluation reruns against the new model version, pinning or routing to a fallback, canary rollout of the fix, and a postmortem that adds the case to the golden dataset. Then ask about a platform they ran and its worst incident.

When do you need an LLMOps engineer?

When several LLM applications or agents run in production; when model or prompt changes need controlled rollout; when AI cost needs attribution and control; when provider incidents have caused outages; and when quality incidents have no owner. One engineer on a platform team can support many product teams by owning shared infrastructure and standards. The maturity path is in the mlops maturity checklist.

How does the role fit with other roles?

AI engineers build applications on the platform; evaluation engineers own datasets and graders that the LLMOps pipelines run; security engineers set policies the gateway enforces; the LLMOps engineer owns the platform and its operation. Adjacent guides are hire ai engineers and hire ai evaluation engineers.

What engagement models fit?

Full-time hires suit organizations with an internal platform team. Embedded partner engineers stand up the gateway, observability, evaluation pipelines, and deployment process and transfer operations. Managed platform operations from a partner suit organizations that want the platform run for them under agreed service levels. Embedded delivery is in the forward deployed engineering playbook.

What drives the cost?

Platform engineering seniority, experience with probabilistic systems, on-call scope, location, and engagement model. LLMOps engineers with production incident experience are scarce; embedded and managed models are common entry points. Verify current market rates. Cost framing is in forward deployed engineer salary.

What are the red flags?

Monitoring limited to error rates and latency with no quality signal; deployments without evaluation gates; no rollback rehearsal; cost visible only as a monthly bill; and unfamiliarity with prompt and configuration versioning. Ask how they would know a model update degraded quality before users did.

What should the first 90 days look like?

In the first month the engineer puts a gateway in front of existing traffic and attributes cost by feature. By day 60 tracing and quality sampling run on every system, evaluation gates releases, and a canary rollout has completed. By day 90 fallbacks have been tested with fault injection, an incident has been handled with a postmortem, and the platform has documented runbooks and service levels.

How FISTA Solutions provides LLMOps engineers

FISTA Solutions embeds LLMOps engineers who stand up gateways, tracing, quality sampling, evaluation pipelines, progressive deployment, and cost controls, operate them to availability targets, and transfer operations to client platform teams. The AI enablement practice delivers the platform, AI agents run on it, and forward deployed engineers embed with client teams. The record behind the approach is 150+ projects with 99.9% uptime.

To run your LLM systems with the discipline your other production systems get, message FISTA on WhatsApp, or read the ai observability checklist for what the role instruments first.