We're partnering with a rapidly scaling and innovative leader in the Digital Media and Entertainment sector, dedicated to optimising user experience and content recommendation through cutting-edge Machine Learning. For the right candidate with the necessary skills and experience, we are pleased to offer 482 visa sponsorship.
This client requires a Data AI/ML Engineer to bridge the gap between data science and production engineering. You will be instrumental in designing the MLOps platform, building robust feature pipelines, and deploying high-performance ML models (such as recommendation engines and user prediction systems) into a live, high-traffic environment. This role demands expertise in both cloud data architecture and production machine learning best practises.
What You'll Do
- Design and build scalable, automated data pipelines (ETL/ELT) for feature engineering, training, and model serving using cloud services like AWS Glue and EMR.
- Lead the deployment and operationalisation of machine learning models (MLOps) into production environments, utilizing platforms like AWS SageMaker for continuous integration and continuous delivery (CI/CD).
- Develop and maintain feature stores and real-time data services to ensure low-latency model prediction serving.
- Collaborate closely with data scientists to transition experimental models into resilient, production-ready code, focusing on performance, scalability, and cost optimisation.
- Implement monitoring and alerting for model performance, data drift, and data quality in production.
- Champion MLOps and DevSecOps practises for the ML platform, ensuring code quality, security, and reproducibility across the entire model lifecycle.
- Contribute to architectural decisions for the overall data and ML infrastructure.
What You'll Bring
- 4+ years of professional experience in Data Engineering or ML Engineering, with a proven track record of deploying models into production.
- Expert proficiency in Python and deep experience with ML frameworks such as TensorFlow or PyTorch.
- Mandatory hands-on experience with AWS cloud services for data and ML (e.g., SageMaker, EMR, S3, Lambda).
- Strong experience with the MLOps lifecycle and tools for model management, versioning, and monitoring.
- Expert-level SQL proficiency and solid understanding of data warehousing and data lake architectures.
- Familiarity with containerisation (Docker) and orchestration (Kubernetes) for model deployment.
- Excellent communication skills, with the ability to articulate complex technical requirements to data scientists and software engineers.
