AI Engineer

11 hours ago

Melbourne, AustraliaSenior

Responsibilities

Build, test, deploy, and maintain production AI solutions using maintainable, well-tested code.
Design and build agentic AI solutions with orchestration frameworks and Model Context Protocol, including tool calling, planning, memory, state management, and human-in-the-loop controls.
Integrate large language models such as Anthropic Claude, Azure OpenAI, and OpenAI into clinical and operational workflows through secure APIs.
Build on the CDAI platform using governed Databricks lakehouse data, Unity Catalog, the enterprise API gateway, and MCP servers.
Prepare structured and unstructured clinical, document, and operational data for model development, evaluation, and retrieval.
Build, train, and validate traditional machine learning models for classification, forecasting, and NLP where appropriate.
Implement LLMOps and MLOps, including versioning, experiment tracking, deployment, observability, cost and latency monitoring, and retraining.
Develop prototypes and proofs of concept through the AI Incubator and contribute reusable components to the AI Accelerators library.
Document implementation patterns, pair with colleagues and partners, and support staff using self-service AI tools.

At least 5 years of professional experience in software, data, or machine learning engineering, including at least 2 years building AI solutions.
Ownership of at least one AI-enabled solution from prototype through production deployment and support.
Advanced Python skills plus working capability in TypeScript/JavaScript, C#, or Java; experience building tested services, APIs, and lightweight user-facing applications.
Strong SQL and data-wrangling skills.
Experience integrating large language model APIs, including authentication, rate limiting, streaming, structured outputs, and error handling.
Hands-on experience building agentic AI solutions using orchestration frameworks and/or MCP, including tool calling, state management, multi-step workflows, human-in-the-loop controls, failure handling, and evaluation.
Practical experience building RAG solutions with vector databases or indexes such as Databricks Vector Search, Azure AI Search, or pgvector, plus embedding models.
Strong prompt, context, and harness engineering skills are desirable.
Experience across the traditional machine learning lifecycle, including feature engineering, training, validation, and deployment, using scikit-learn, PyTorch, or Hugging Face.
Experience in technical discovery and workflow analysis, preferably from consulting, implementation, or product engineering work directly with users, is highly regarded.
Completion of required pre-employment checks, including relevant immunisations, is required.

Permanent, full-time position based in Melbourne with hybrid working and a minimum of one day per week on site.
Competitive salary packaging options.
Discounted private health insurance.
Employee Assistance Program for staff and their families.
Access to the Fitness Passport.
Applications are progressed throughout the advertising period, with a closing date of 5 November 2026 at 11:59pm.

1,001-5,000 employees