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MLOps Engineer

3 weeks ago

Brisbane, Queensland, Australia Humanised Group Full time

Hands-on Databricks, supporting production ML products within an established data team right here in Brisbane

This contract role supports machine learning products already running in production. It's a blend of BAU platform support, deployment governance, and technical enablement for Data Scientists working in Databricks.

This is not a pure Data Scientist role, it's ideal for someone who enjoys being the technical backbone of a data platform.

What you will be working on:

  • Supporting Databricks based ML products in production with in initial BAU focus
  • Transitioning models from Databricks notebook prototypes into production utilising ML and pipeline best practices.
  • Design, develop, deploy and maintain end to end machine learning and data pipelines
  • Manage data and data pipeline operations including data migration, resource management, data pipeline configurations, and troubleshooting
  • Collaborate with Data Scientists, Data Engineers, DevOps Engineers and Application developers to design, build, deploy, and maintain robust solutions to critical problems
  • Write and review technical documents, including design, development, and collaborative code reviews.
  • Monitor and enhance the performance, cost, stability, and operational efficiency of existing tools and services.
  • Maintenance of the Databricks infrastructure (including UC catalogue, compute, users and updates).

Experience:

  • 3+ years in a DevOps or platform role within AWS environments
  • Understanding of cloud-native, Agile and DevOps operating models
  • Experience coding, scripting in Python, Bash or PowerShell
  • CI/CD pipeline automation using Azure DevOps
  • Infrastructure as Code experience using Terraform
  • Experience with cloud monitoring and logging - CloudWatch
  • Strong Python and Spark experience, including Databricks
  • Experience deploying and supporting ML models in production
  • Solid understanding of the ML model lifecycle
  • Experience with MLflow and Feature Stores
  • Background in big data, scalable or shared system design
  • Experience working with relational and non-relational data stores across varying data speeds
  • Familiarity with unit testing, agile delivery, and change/incident management
  • Ability to manage multiple priorities and projects effectively

Qualifications:

  • Degree in Computer Science, or similar, or equivalent practical experience
  • IT or quantitative undergraduate degree
  • AWS Cloud Certifications (Cloud Practitioner)
  • Databricks certifications (Data Engineering Associate, Machine Learning Associate)