Full Stack Machine Learning Engineer

3 weeks ago

City of Brisbane, Australia P&C Partners Pty Ltd Full time

Overview

Cloud-based platform delivering AI-powered predictive maintenance and asset reliability for large-scale industrial operations.

The Company

This fast-growing, private-equity-backed SaaS business is redefining how asset-intensive industries harness data and AI to drive reliability and performance. Their enterprise platform is trusted by global leaders, delivering smarter, faster, and more accurate insights for large-scale operations. With a modern technology stack, a collaborative engineering culture, and an ambitious growth trajectory, they offer the chance to help shape the future of industrial intelligence.

The Role

As a Full Stack Machine Learning Engineer, you’ll join a high-performing R&D team working on mission-critical AI solutions for predictive maintenance and operational optimisation. Reporting to the Manager of AI & Data Science, you’ll design, build, and deploy production-grade ML models that power real-time decision support and automation across complex environments. You’ll work hands-on with one of the world’s richest industrial datasets, collaborating with domain experts and product teams to deliver solutions that make a measurable impact in the field.

About You

You’re a technically strong and ambitious ML engineer who thrives in collaborative, fast-paced environments. You enjoy solving complex problems, building scalable solutions, and working across modern frameworks and cloud platforms. With a solid grounding in end-to-end ML development and a passion for deploying models that deliver real-world results, you’re eager to keep learning and contribute ideas that push the boundaries of industrial AI.

Skills & Experience

- 3+ years of experience in machine learning engineering, including at least 1 year in industrial applications (IoT, predictive analytics, fleet telemetry, or similar)
- Strong proficiency in Python for ML development
- Experience with ML frameworks (TensorFlow, PyTorch) and libraries (scikit-learn, Hugging Face)
- Proven track record of deploying ML models into production (time-series forecasting, anomaly detection, classification)
- Hands-on experience with MLOps tools (MLflow, Kubeflow, SageMaker, or similar) for model versioning, monitoring, and retraining
- Familiarity with agentic AI concepts (multi-agent systems, human-in-the-loop implementations)
- Understanding of data pipelines and ETL for multimodal datasets (sensor, image, text, telemetry)
- Cloud deployment experience (AWS, Azure, or GCP) with containerisation (Docker, Kubernetes)
- Knowledge of interpretable ML techniques (e.g., SHAP, LIME)

Valuable extras

- Experience with predictive maintenance or condition monitoring
- Exposure to computer vision (thermal imaging, defect detection)
- Familiarity with graph neural networks or multimodal AI
- Contributions to open-source ML projects or publications in industrial AI

What’s on Offer

- Competitive salary and performance bonus
- Flexible hybrid working arrangements
- Dedicated professional development opportunities
- Inclusive, collaborative company culture
- Support for work-life balance, including extra leave and wellbeing initiatives

Apply Now

If you’re ready to make an impact in a fast-growing SaaS business and help deliver AI solutions that transform asset reliability and performance, apply today Click “Apply Now” or reach out to the Brisbane office for a confidential discussion.

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