Company Description
Showtime Consulting is a leading provider of Shielded Cloud and Digital Solutions across Australia and New Zealand. We specialise in delivering secure, enterprise-scale technology solutions across cloud, infrastructure, cybersecurity, DevSecOps, software engineering, data, AI/ML, and digital transformation programs.
We partner with government and enterprise organisations to build high-performing technology teams that deliver secure, scalable, and future-ready solutions within complex technology environments.
The Role
We are seeking an experienced GCP Data & MLOps Engineer to support the design, development, and delivery of modern data, AI/ML, and cloud-native platform solutions.
This role is suited to a hands-on engineer with strong experience across Google Cloud Platform, Dataflow, Apache Beam, BigQuery, Kubeflow, Python, Django, and DevOps/MLOps practices. You will work across data pipeline development, AI solution delivery, model lifecycle management, cloud automation, dashboard deployment, and application development to support scalable and efficient platform outcomes.
Key Responsibilities
Design, build, and maintain customised data pipeline frameworks using Google Cloud Platform.
Develop scalable data processing solutions using Dataflow, Apache Beam, Python, and BigQuery.
Build and maintain Dataflow Flex Template Frameworks and domain-specific Python libraries.
Perform BigQuery data extraction, preprocessing, and automated job scheduling.
Design and implement Kubeflow Pipelines for automated ML pipeline creation and deployment.
Deliver AI-based solutions on the GCP platform.
Apply DevOps and MLOps practices across the data and machine learning lifecycle.
Implement automation and monitoring across data flows, ML systems, and cloud services.
Manage model lifecycle, maintenance, and governance using GCP Model Registry.
Enable continuous deployment of Django applications on GCP.
Automate deployment and management of Cloud Run services and Cloud Functions.
Implement automated CI/CD pipelines using Cloud Build.
Develop REST API-based microservices to support platform and application integration.
Implement Secret Manager and Cloud KMS, including deterministic encryption.
Develop customised web applications for AI Platform resource creation and management.
Deploy and maintain dashboards using R Shiny and Python Django.
Optimise application code, data pipelines, and technical deliverables to improve performance and efficiency.
What You'll Need
Strong hands-on experience with Google Cloud Platform.
Experience developing customised data pipelines using Dataflow and Apache Beam.
Strong Python development skills, including experience building reusable Python libraries.
Experience working with BigQuery for data extraction, preprocessing, and automated scheduling.
Experience building Dataflow Flex Template Frameworks is highly regarded.
Experience delivering AI-based solutions on GCP is advantageous.
Strong understanding of DevOps and MLOps practices.
Experience applying DevOps methodologies to machine learning systems.
Experience designing and implementing Kubeflow Pipelines is highly regarded.
Experience with GCP Model Registry and model lifecycle management is advantageous.
Experience developing microservices using REST APIs.
Experience with Python Django application development is highly regarded.
Experience automating Cloud Run and Cloud Functions deployments.
Experience with automated Cloud Build implementations.
Knowledge of Secret Manager, Cloud KMS, and deterministic encryption is advantageous.
Experience deploying dashboards using R Shiny or Python Django is highly regarded.
Experience developing customised web applications for AI platform resource management is advantageous.
Strong understanding of cloud-native application development and platform automation.
Strong analytical, troubleshooting, optimisation, and problem-solving skills.
Good communication skills with the ability to work across technical and business teams.
Why Join Us?
Work on modern GCP-based data, AI/ML, and platform engineering solutions.
Build scalable cloud-native data pipelines and machine learning workflows.
Gain exposure to Dataflow, Apache Beam, Kubeflow, BigQuery, and MLOps practices.
Contribute to automation, CI/CD, dashboarding, and cloud platform modernisation initiatives.
Collaborate with experienced cloud, data, software engineering, and delivery teams.
Join a consulting culture focused on innovation, technical excellence, and continuous improvement.
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