AI/Machine Learning Engineers

AI/Machine Learning Engineers required in capital cities throughout Australia. You will be responsible for designing, developing, deploying, and maintaining high-performance machine learning models and AI-driven solutions that directly impact business strategy. These roles are critical components in the digital transformation, focusing on production-ready systems that utilise modern cloud data architecture.

MUST HOLD VALID AUSTRALIAN WORK RIGHTS AND CURRENTLY RESIDE IN AUSTRALIA

Key Responsibilities

Model Development: Design, build, train, and test robust Machine Learning (ML) models (e.g., predictive models, classification, forecasting) using Python and standard ML frameworks.

Production Deployment: Implement models into production using best practices for MLOps, ensuring scalability and reliability on AWS or Azure cloud platforms.

Data Engineering: Work closely with data engineers to access, transform, and manage large-scale datasets stored in Snowflake for model training and inference.

Code Quality: Write clean, well-documented, and efficient Python code, utilising libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow/PyTorch.

Infrastructure: Manage cloud resources for ML workloads, including setting up compute environments, data pipelines, and security protocols on AWS (e.g., SageMaker) or Azure (e.g., Azure ML).

Collaboration: Partner with business stakeholders and product owners to translate complex business problems into viable AI solutions.

Essential Technical Requirements
The ideal candidates will possess 3+ years of professional experience in an AI/ML engineering role and demonstrable proficiency in the following:

Machine Learning (ML): Deep experience with model training, validation, MLOps, A/B testing, and strong understanding of deep learning fundamentals.

Programming: Expert-level proficiency in Python is mandatory, including common libraries like Scikit-learn, Pandas, NumPy, and TensorFlow/PyTorch.

Cloud Platforms: Strong deployment experience in either AWS (e.g., S3, EC2, SageMaker) OR Azure (e.g., Azure ML, Azure Data Factory).

Data Warehouse: Proven experience working with data housed in Snowflake, including strong SQL skills for data ingestion and manipulation.

Tools/Other: Experience with Git/GitLab, Docker/Kubernetes, REST API integration, and data visualisation tools.

Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.

Relevant cloud certifications (e.g., AWS Certified Machine Learning Specialist or Azure AI Engineer) are highly advantageous.

If you're ready to bring structure, energy, and precision to a fast-paced project team, we’d love to hear from you.