Enterprise AI Deployment
AgentOps / LLMOps / AI Reliability Engineer
Make AI systems observable, testable, cost-aware, secure and dependable after they leave the prototype environment.
About this path
This role owns evaluation pipelines, release controls, monitoring, tracing, incident response, latency, cost management and safe fallback. Model quality can be probabilistic, but production accountability cannot.
You will own what happens to AI systems after they leave the prototype stage, including evaluation pipelines, monitoring, tracing, incident response and cost management. Model outputs may be probabilistic, but your accountability for production reliability is not. This role fits engineers who think in terms of dashboards, alerts and safe fallbacks, not just capability.
What you would own
- Evaluation pipelines for monitoring model quality over time
- Release controls and rollout safety for AI-driven features
- Monitoring, tracing and alerting for AI system behavior
- Incident response when AI systems misbehave or degrade
- Latency and cost management across AI infrastructure
- Safe fallback design for when models fail or underperform
You are likely a strong match if
- You have production experience in DevOps, SRE or MLOps
- You have built or maintained evaluation pipelines for model quality
- You think in terms of monitoring, alerting and incident response
- You understand cost and latency tradeoffs in production AI systems
- You design fallback paths rather than assuming the model will work
- You are comfortable being on call for production issues
Helpful, not required
- Experience with observability platforms such as Datadog or Grafana
- Experience with LLM-specific tracing tools
- Familiarity with model evaluation frameworks
- Cloud infrastructure and cost-management experience
What success looks like
- AI system incidents are caught and resolved before major impact
- Evaluation pipelines catch quality regressions before users do
- Cost and latency stay within agreed operating budgets
- Fallback paths activate correctly when models underperform
Practical proof that helps
- A description of an incident you resolved in a production AI or software system
- Examples of monitoring or evaluation pipelines you have built
- Metrics showing cost, latency or reliability improvements you drove
What being in the network gives you
- Remote-first work with clients across the United States, Canada and Latin America.
- Human review of your profile, automation organizes information, people decide.
- One profile considered across current and future opportunities.
- Referral rewards when someone you refer directly is successfully placed.
- Full control over availability, matching and your data at any time.
One profile, many opportunities
Applying here creates a single reusable profile. If this path is not the right fit, you remain eligible for other suitable opportunities across the network.
Apply and join the network
Free to join • Start in about 60 seconds • One profile for multiple opportunities • No advanced AI experience required for many roles
Joining the network does not guarantee immediate work or placement. Opportunities depend on professional fit, location, availability and client demand.