We are partnering with an enterprise client to accelerate their artificial intelligence and machine learning capabilities. We are seeking a skilled AI / Machine Learning Engineer to represent our organisation and take technical ownership of designing, building, and deploying production-grade AI models, LLM pipelines, and scalable ML infrastructure.

In this role, you will bridge data science, software engineering, and MLOps within our client’s ecosystem. You will be instrumental in translating complex business requirements into high-performing Machine Learning models and generative AI workflows that deliver real-world impact.

? Visa & Sponsorship OptionsAs the employer of record, we provide visa pathways for qualified engineering talent deployed to our clients:

- 482 On-Hire Sponsorship Transfers: Fully supported for qualified candidates currently in Australia on an existing 482 visa looking to transfer sponsorship to work with our clients.
- New 482 Visa Sponsorship: Available for qualified candidates with the requisite commercial experience and technical skills.
- Onshore Applicants: Open to onshore candidates seeking stable enterprise client engagement.

Core Responsibilities
- Model Development & • Fine-Tuning:

Design, train, fine-tune, and evaluate machine learning models and large language models (LLMs) to solve complex enterprise problems.
- MLOps & • Pipeline Automation:

Build robust, reproducible MLOps pipelines using tools like MLflow, Kubeflow, or cloud-native MLOps services for continuous model training, deployment, and monitoring.
- GenAI & • RAG Architecture:

Design and deploy Generative AI applications,



Retrieval-Augmented Generation (RAG) pipelines, and vector database solutions (e.g., Pinecone, Qdrant, Chroma).
- API & • System Integration:

Develop scalable REST/gRPC APIs using Python (FastAPI, Flask) to serve model inference endpoints cleanly into modern microservice architectures.
- Monitoring & • Drift Control:

Implement real-time monitoring for model performance, data drift, and bias to ensure enterprise-grade reliability and governance.
- Client Engagement & • Collaboration:

Partner closely with client product teams, data engineers, and domain experts to align ML architectures with strategic goals.

Selection Criteria
- ML & AI Mastery: Strong, hands-on professional experience with modern machine learning frameworks (PyTorch, TensorFlow, Scikit-Learn) and NLP/GenAI architectures.
- Software Engineering: Production-level coding proficiency in Python with robust software design principles, unit testing, and Git practices.
- Cloud & MLOps: Practical experience deploying ML models to major cloud platforms (AWS, Azure, or GCP) using containerisation (Docker, Kubernetes) and MLOps tools.
- Data Pipelines: Solid understanding of data engineering patterns, SQL/NoSQL databases, and managing large datasets for model training.
- Location Requirements: Currently residing in Australia with valid work rights or eligibility for a 482 visa sponsorship transfer.

Preferred Qualifications (Nice to Have)
- Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex).
- Experience in highly regulated industries (Finance, Telecommunications, Healthcare).
- AWS / Azure AI & ML Certifications.

📌 AI / Machine Learning Engineer (Sydney)
🏢 AI Talent
📍 Sydney