Machine Learning Engineer

• Tech stack includes Databricks, Apache Spark, MLflow and R

NTT Data are seeking an experienced M/LOps Engineer to join a major data and analytics program delivering advanced forecasting and predictive modelling capabilities.

• Contract through to end of June 2027
• Perth CBD location, ASAP start

This is an exciting opportunity to work at the intersection of machine learning, software engineering and cloud-scale data platforms, helping to productionise, automate and optimise analytical solutions within a modern Databricks environment.

Working closely with data scientists, data engineers and architects, you'll play a key role in building scalable machine learning pipelines, automating model lifecycles and ensuring analytical workloads are reliable, secure and production-ready.

Key Responsibilities

• Build, test and optimise production-grade machine learning and data processing pipelines using Databricks and Apache Spark
• Design and implement MLOps frameworks supporting model deployment, versioning, monitoring and automated retraining
• Develop scalable and maintainable solutions using software engineering best practices, including automated testing and CI/CD
• Optimise and productionise R-based forecasting and statistical modelling workloads using Sparklyr and SparkR
• Collaborate with data architects, engineers and business stakeholders to integrate predictive models into downstream systems
• Monitor platform performance, pipeline reliability and data integrity across production environments
• Manage Databricks workloads, MLflow implementations and model lifecycle processes
• Support data governance, lineage and operational best practices across machine learning solutions
• Contribute to architecture discussions and continuous improvement of engineering standards

Skills & Experience

• Strong software engineering background with experience in design patterns, testing, Git and CI/CD practices
• Proven experience working within the Databricks ecosystem including MLflow, Unity Catalog and workload orchestration
• Strong hands-on experience with Apache Spark and distributed data processing
• Advanced R development skills, including experience with Sparklyr or SparkR
• Experience building and supporting production machine learning or advanced analytics solutions
• Strong understanding of large-scale data processing, Delta Lake and complex data environments
• Experience working with time-series, forecasting or statistical modelling workloads
• Excellent problem-solving, communication and stakeholder engagement skills

Nice to Have

• Python and PySpark experience
• Experience with cloud platforms such as Azure, AWS or GCP
• Exposure to Infrastructure as Code tools such as TerraformUnderstanding of model drift monitoring, data lineage and enterprise data governance frameworks

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