**Job Overview**
This private-equity-backed SaaS business is revolutionizing how asset-intensive industries harness data and AI to drive reliability and performance. Their enterprise platform delivers smarter faster and more accurate insights for large-scale operations.
The Role:
As a full stack machine learning engineer, you will join a high-performing R&D team working on mission-critical AI solutions for predictive maintenance and operational optimization. You will design, build, and deploy production-grade ML models that power real-time decision support and automation across complex environments.
About the Job:
You are 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 are eager to keep learning and contribute ideas that push the boundaries of industrial AI.
Required Skills and Qualifications:
* 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
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
Requirements: No prior experience required but some knowledge about industrial machines can be beneficial.