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Melbourne
3 days ago
Senior LLMOps Engineer logo

Senior LLMOps Engineer

Heidi

Melbourne
3 days ago
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Senior LLMOps Engineer

Heidi is seeking a Senior LLMOps Engineer to join the model team in Melbourne, Australia. The role involves building the operational layer around production LLMs, including deployment health dashboards, tracing, feedback flywheels, and per-model unit economics. Candidates should have 2-3 years of hands-on LLMOps experience at an AI company operating at or beyond Heidi's maturity.

Core AIOn-siteFull-timeSeniorLLMOpsObservability

Senior LLMOps Engineer

Heidi is seeking a Senior LLMOps Engineer to join the model team in Melbourne, Australia. The role involves building the operational layer around production LLMs, including deployment health dashboards, tracing, feedback flywheels, and per-model unit economics. Candidates should have 2-3 years of hands-on LLMOps experience at an AI company operating at or beyond Heidi's maturity.

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Core AIOn-siteFull-timeSeniorLLMOps

Salary

Not specified

Work Location

Melbourne, Victoria, Australia, AU

Work Model

On-site

Experience Required

3 years

Employment Type

Full-time

Experience Level

Senior

Core Qualifications

Technical (Must-have)
LLMOpsObservabilityMonitoringAlertingDatadogDistributed TracingLLM PipelinesData ModelingLLMsAI AgentsUnit EconomicsIncident ResponseEvaluationFeedback Systems
Soft Skills
OwnershipLeadershipProblem SolvingAmbiguity ManagementCollaboration

Preferred Qualifications

Technical (Nice-to-have)
Backend EngineeringData PlatformML InfrastructureIntercomHealthcare

Key Responsibilities

  • •Build the deployment health dashboard with live visibility into every model in production.
  • •Make every incident traceable with complete session-to-model lineage.
  • •Stand up the improvement flywheel using Intercom tickets and CSAT feedback.
  • •Surface the full story by retrieving execution traces for flagged sessions and generating summaries.
  • •Close the loop by filtering high-value feedback into training data and monitoring post-deployment performance.
  • •Own per-model P&L by measuring revenue against inference cost.
  • •Raise the LLMOps bar by bringing proven practices from mature AI companies.
  • •Partner across the model team to ensure observability is built in.
LLMOpsAIHealthcareEngineeringSeniorOn-siteFull-timeObservabilityLLMsMelbourne
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