Heidi Health14 followers58 jobs

Senior LLMOps Engineer

SeniorFull-timeHybridMelbourne VIC, AustraliaInformation technologySoftware engineeringPosted 1 day ago
Pending fitX of Y criteria met

About the job

As a Senior LLMOps Engineer at Heidi, you will play a crucial role in building and scaling the infrastructure that powers our AI Care Partner. Your work will directly impact healthcare professionals by ensuring they can focus on what matters most: their patients. The team thrives on collaboration, innovation, and a shared commitment to creating a reliable and efficient platform.

Meet the team

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Think you're a good fit?See what the hiring team are looking for

You'll be responsible for

🚀

Leading LLMOps platform development

Lead the architecture, design, and implementation of our end-to-end LLMOps platform, from data ingestion and model training pipelines to production deployment and monitoring.
🔄

Automating the LLM lifecycle

Build and maintain robust CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines to automate the testing, validation, and deployment of large language models.
📈

Ensuring scalable and reliable deployment

Engineer highly available and scalable model serving solutions using modern infrastructure like Kubernetes, ensuring low latency and high throughput for our production services.

Key criteria

🔧

MLOps infrastructure experience

Proven track record in designing and maintaining MLOps infrastructure.

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☁️

Cloud-native infrastructure expertise

Hands-on experience building scalable cloud-native platforms.

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📊

Large-scale model deployment

Experience deploying and managing large-scale machine learning models.

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View your fit

5 criteria for this job
MLOps infrastructure experience
Proven track record in designing and maintaining MLOps infrastructure.

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Cloud-native infrastructure expertise
Hands-on experience building scalable cloud-native platforms.

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Large-scale model deployment
Experience deploying and managing large-scale machine learning models.

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Python and containerization skills
Expertise in Python, Docker, and Kubernetes for automation.

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Machine learning lifecycle knowledge
Deep understanding of the entire machine learning lifecycle.

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A meaningful career starts with a match

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