Production-Grade AI Agent Engineer for Healthcare

Sierra; Melbourne; Hybrid; AUD 150.000 - 210.000; Vollzeit

Benefits dieser Stelle

Salary packaging options

Private health insurance

Employee Assistance Program

Fitness Passport access

Zusammenfassung

St Vincent’s Health Australia seeks a senior AI/ML engineer to join the AI Delivery team in Melbourne. You will take AI solutions from prototype to governed production, collaborating with clinicians, governance, privacy and legal teams.

You will work with Databricks, Azure, MCP, and generative AI models, building production-ready tools, orchestrating workflows, and embedding AI across clinical and operational workflows.

Qualifikationen

• Minimum 5 years’ 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.
• Proven experience integrating large language model APIs, such as Anthropic, Azure OpenAI or OpenAI, into applications, including authentication, rate limiting, streaming, structured outputs and error handling.
• Hands-on experience building agentic AI solutions with 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, and embedding models.
• Strong prompt, context and harness (desirable) engineering skills
• Experience across the traditional machine learning lifecycle, including feature engineering, training, validation and deployment, using libraries such as scikit-learn, PyTorch or Hugging Face.
• Experience in technical discovery and workflow analysis, ideally gained in a consulting, implementation or product engineering role working directly with users, would be highly regarded.

Aufgaben

• Build, test and deploy production AI solutions, turning architecture designs and standards into maintainable, well-tested code.
• Design and build agentic AI solutions using orchestration frameworks and Model Context Protocol (MCP), including tool calling, multi‑step planning, memory and human‑in‑the‑loop controls.
• Integrate large language models, including Anthropic Claude through the Databricks Mosaic AI Gateway and Azure OpenAI, into clinical and operational workflows through secure, documented APIs.
• Build on the CDAI platform, consuming governed data products from the Databricks lakehouse through Unity Catalog and exposing AI capabilities through the enterprise API gateway and MCP servers.
• Prepare structured and unstructured data, including clinical notes, documents and operational data, for model development, evaluation and retrieval.
• Build, train and validate traditional machine learning models, such as classification, forecasting and NLP, where they suit the problem better than Generative AI.
• Implement LLMOps and MLOps across the full lifecycle, including versioning of prompts, models and datasets, experiment tracking with MLflow, deployment, observability, cost and latency monitoring, and retraining.
• Build prototypes and proofs of concept through the AI Incubator to test the feasibility and value of new ideas, and contribute reusable components to the team’s library of AI Accelerators.
• Build internal capability by documenting patterns, pairing with colleagues and implementation partners, and supporting divisional staff who use the platform’s self‑service AI tools.

Kenntnisse

Software engineering

Machine learning engineering

Generative AI

Data governance

Cloud engineering

Tools

Databricks

Azure

MCP

OpenAI API

MLflow

Jobbeschreibung

St Vincent’s Health Australia seeks a senior AI/ML engineer to join the AI Delivery team in Melbourne. You will take AI solutions from prototype to governed production, collaborating with clinicians, governance, privacy and legal teams.

You will work with Databricks, Azure, MCP, and generative AI models, building production-ready tools, orchestrating workflows, and embedding AI across clinical and operational workflows.