Senior AI Engineer

H2O.ai

Sydney

3 weeks ago

Sydney

3 weeks ago

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Senior AI Engineer

H2O.ai is seeking a Senior AI Engineer for a remote role based in Sydney. The engineer will design and ship end-to-end AI solutions for enterprise customers, focusing on agentic AI, LLM applications, and production ML pipelines. Requires 3+ years of AI/ML engineering experience and strong Python skills.

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Remote

Full-time

Mid Level

Python

PyTorch

Salary

Not specified

Work Location

Sydney, New South Wales, Australia, AU

Work Model

Remote

Experience Required

3 years

Employment Type

Full-time

Experience Level

3+ years hands-on AI/ML engineering

Core Qualifications

Technical (Must-have)

PythonPyTorchTensorFlowscikit-learnLangChainLlamaIndexDockerKubernetesREST APIsCI/CD

Soft Skills

problem solvingcommunication

Preferred Qualifications

Technical (Nice-to-have)

H2O.ai productsWaveH2O Document AI

Key Responsibilities

•Design and build agentic AI systems and multi-agent frameworks for enterprise customers.
•Develop and deploy LLM-powered applications using RAG, fine-tuning, prompt engineering, function calling, and tool use.
•Implement guardrails, evaluation frameworks, and responsible AI controls.
•Own the full development lifecycle: problem framing, data exploration, model development, API integration, and production deployment.
•Build scalable backend services and APIs for AI capabilities.
•Integrate AI models into cloud, on-prem, and hybrid customer environments.
•Develop ML pipelines and LLMOps infrastructure for continuous model improvement and monitoring.
•Work directly with customer teams to translate problems into AI solutions.
•Contribute to pre-sales and proof-of-concept engagements.
•Communicate clearly across technical and non-technical audiences.

Senior AI EngineerAILLMAgentic AIPythonMachine LearningEnterpriseRemoteSydneyH2O.ai

Key Responsibilities

•Design and build agentic AI systems and multi-agent frameworks for enterprise customers.

•Develop and deploy LLM-powered applications using RAG, fine-tuning, prompt engineering, function calling, and tool use.

•Implement guardrails, evaluation frameworks, and responsible AI controls.

•Own the full development lifecycle: problem framing, data exploration, model development, API integration, and production deployment.

•Build scalable backend services and APIs for AI capabilities.

•Integrate AI models into cloud, on-prem, and hybrid customer environments.

•Develop ML pipelines and LLMOps infrastructure for continuous model improvement and monitoring.

•Work directly with customer teams to translate problems into AI solutions.

•Contribute to pre-sales and proof-of-concept engagements.

•Communicate clearly across technical and non-technical audiences.