The setting
sells software to mid-market and large businesses. Its hold customer conversations and take actions in the business's connected systems, across digital and voice channels.
The same capacity that protects live conversations can obscure why one service is becoming expensive.
Customer-agent availability needs headroom while the customer needs a defensible explanation for rising infrastructure spend.
Who you’d be doing this for
“Finance asked why this service jumped again, and I still can’t tell them what changed.”
Dina Hassan · Customer Support Operations Director · a character in this brief
Owns service coverage and budget accountability for the enterprise customer running agents in an isolated environment.
What is at stake
One inference-serving service is raising cloud spend while customer contact volume stays steady. You must decide whether AI recommends capacity changes or only prepares evidence for each review.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- capacity headroom vs. explainable spend
- standard guardrails vs. environment-specific evidence
- customer data boundaries vs. operational visibility
- cost discipline vs. conversation availability
Why Sierra
Customer-facing AI agents run in customer environments where reliable infrastructure and explainable operating costs both shape the deployment relationship.
Written with these in mind
Not your kind of problem? 37 more at Sierra, or browse every organization.
Where the organization context comes fromThe postings this brief was written from, and the reporting read before it.
The organization context comes from public information. The situation and characters are written for the brief.
Job postings it was written from
Public descriptions of the work at Sierra. They are listed as sources, not as openings. The lines under a posting are its own words, where it meets this brief.
- Deployed Infrastructure Engineer (opens in a new tab)
- Work with customers' platform engineering, security, and DevOps teams to navigate their infrastructure constraints and compliance requirements.
- Own deployment architecture conversations and execution — designing and implementing deployment strategies across customer environments, including VPC configuration, permissioning, networking, and infrastructure provisioning.
- Software Engineer, Infrastructure (opens in a new tab)
- Ensure the reliability, scalability, and performance of our platform and LLM inference serving as we rapidly grow traffic.
- Build and maintain cloud infrastructure using Terraform to ensure scalable, secure, and reproducible environments.
- IT Infrastructure Engineer (opens in a new tab)
- We’re looking for a hands-on IT Infrastructure Engineer to help scale Sierra’s internal systems during a critical period of growth.
- You’ll help design and implement core infrastructure, strengthen security controls, and improve how we operate internally.
- Software Engineer, Cloud Infrastructure (opens in a new tab)
- Stand up and operate new multi-tenant regions and single-tenant environments for customers with strict regulatory or isolation needs.
- Design, build, and operate Sierra’s cloud infrastructure across AWS and GCP (and soon Azure) using Terraform and Kubernetes.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.
Where similar work appears
Other live briefs connected to this one, and what connects them.
Similar problems at other organizations
Like this one, each of these turns on deciding on evidence that's still thin.
A checkout service's compute bill rose 29% while requests grew 9%.
You decide whether AI can draft a capacity change from observed peaks or must ask engineers to establish the burst allowance first.
Pilot customers cannot tell whether their next 30 days of GPU demand fits their current quota.
You must weigh a useful planning signal against the risk of implying guaranteed capacity.
ML engineers are rebuilding quality and serving-cost evidence every time a premium route is questioned.
You have to weigh a guided decision flow against the flexibility enterprise evaluation practices require.
The same setting, from another role
Each is set around Sierra’s Customer-facing AI agents, written for a different role. They’re separate briefs, not the same scenario.
Enterprise support leaders are waiting on launch answers while account evidence sits across several records.
You must decide whether the AI drafts only from approved guidance or also surfaces tentative working-session details for review.
Billing callers need payment help before service interruptions turn into repeat contacts.
You must weigh earlier self-service against a consent record that holds up when a consumer disputes the arrangement.
A healthcare customer paused its expansion over whether patient conversations can be used to improve the agent.
You must weigh a deal-closing privacy commitment against the investigation record needed after a harmful response.
Other problems with a similar purpose
In each of these, as in this one, the aim is to use evidence to make delivery leaner and more efficient without treating reliability as something to trade away.
Mine operations managers are waiting on fixes while reviews reopen for missing vehicle evidence.
You must decide whether AI marks uncertain evidence or holds the change for an engineer to investigate.
Worker-type concerns are appearing repeatedly across payroll API migration feedback, but their source evidence is scattered.
You must decide where the drafting of clusters from Operations and Compliance feedback stops and where a product manager's own reading of the source examples has to begin.
Daily event-air exceptions are producing conflicting explanations for the same cost and service variance.
You must decide what AI can match and draft, and what reviewers must verify before finance acts.
