4 days ago

Senior AI Models Engineer

Heidi

Melbourne

4 days ago

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Heidi is seeking a Senior AI Models Engineer to build and deploy custom AI systems for enterprise healthcare customers. The role involves model customisation, deployment, agent systems, and customer delivery, requiring strong experience in at least two of these areas. This is a remote full-time position based in Melbourne, Australia.

AI-enabledRemoteFull-timeSeniorModel CustomisationDeployment And Inference

Senior AI Models Engineer

Apply

AI-enabledRemoteFull-timeSeniorModel Customisation

Salary

Not specified

Work Location

Melbourne, Victoria, Australia, AU

Work Model

Remote

Employment Type

Full-time

Experience Level

Senior

Core Qualifications

Technical (Must-have)

Model customisationDeployment and inferenceAgent systemsCustomer deliverySoftware engineeringMachine Learning engineeringOpen-weight language modelsLLM ecosystem

Soft Skills

Clear communicationGood judgementAbility to work through ambiguity

Key Responsibilities

•Turn customer use cases into training and evaluation plans.
•Select suitable data, models, and training approaches.
•Fine-tune and evaluate models for specialised domains.
•Improve quality based on testing and production feedback.
•Design cost-effective cloud or on-prem deployment approaches.
•Select suitable infrastructure and serving configurations.
•Improve reliability, latency, and operating cost.
•Build monitoring, backup, and recovery processes.
•Build agent workflows around language models.
•Connect models with customer data, tools, and existing systems.
•Add the controls and observability needed for production use.
•Work with customers to understand their requirements and constraints.
•Explain trade-offs between quality, cost, privacy, and complexity.
•Help take solutions from early discovery through production launch.

AIHealthcareMachine LearningSoftware DevelopmentSeniorRemoteFull-timeLLMModel CustomisationAgent Systems

Key Responsibilities

•Turn customer use cases into training and evaluation plans.

•Select suitable data, models, and training approaches.

•Fine-tune and evaluate models for specialised domains.

•Improve quality based on testing and production feedback.

•Design cost-effective cloud or on-prem deployment approaches.

•Select suitable infrastructure and serving configurations.

•Improve reliability, latency, and operating cost.

•Build monitoring, backup, and recovery processes.

•Build agent workflows around language models.

•Connect models with customer data, tools, and existing systems.

•Add the controls and observability needed for production use.

•Work with customers to understand their requirements and constraints.

•Explain trade-offs between quality, cost, privacy, and complexity.

•Help take solutions from early discovery through production launch.