MLOps Engineer

7 Eleven; City of Melbourne; Hybrid; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Open office in Richmond

Remote flexibility

Volunteer day off

Free coffee and snacks

Parental leave up to 15 weeks

Social Club & Open Committee

LinkedIn Learning access

Job summary

7-Eleven Australia is seeking an MLOps Engineer to help deploy, operate and maintain machine learning solutions in production. You will work with Data Scientists and Engineers to bridge model development and production, focusing on a Databricks-based engine and a growing portfolio of predictive and AI use cases.

You will implement production workflows, build CI/CD pipelines, and ensure secure, scalable data and feature pipelines while supporting Generative AI initiatives and tooling across the

Qualifications

• Hands-on experience deploying data science and ML solutions in production.
• Proficiency with Python, SQL and PySpark in distributed environments.
• Experience with Databricks ecosystem and model deployment in production.

Responsibilities

• Partner with Data Science to take models from experimentation through deployment and production management.
• Implement and maintain production workflows for data ingestion, processing, model execution and retraining.
• Build and maintain CI/CD pipelines and controlled release processes for ML workloads.
• Create robust data and feature pipelines for ML/AI solutions.
• Diagnose production issues and coordinate with teams to resolve model, data and platform problems.
• Enforce access controls, security and governance within Databricks and AI systems.
• Support operationalising Generative AI solutions, including LLM applications and RAG.
• Contribute to reusable templates, tooling and MLOps practices.
• Establish best practices and state-of-the-art tooling for production standards.
• Assist performance optimisation and efficient use of Databricks and cloud infra.

Skills

Python

SQL

PySpark

CI/CD

Databricks

Azure Cloud

Governance & security

Model deployment

Tools

Azure Databricks

MLflow

Delta Lake

Unity Catalog

Workflows

Azure DevOps

CI/CD tooling

Job description

Since 2024, 7-Eleven Australia has joined 7-Eleven international to be part of the biggest retail network across the world, represented in 20 countries with over 84,000 stores. We have big growth plans in Australia and a lot of opportunity for someone who wants to be part of ever growing retailer with a global footprint.

Firstly, what we offer you!

Vibrant Open Office in Richmond.Work in a dynamic, collaborative space that sparks creativity

Work Your Way.Enjoy the perfect balance of remote flexibility and in-office collaboration—get the best of both worlds

Make a Difference.Take a paid day off each year to volunteer for a cause you’re passionate about

Fuel Your Day.Enjoy free 7-Eleven coffee and snacks in the office—because great ideas start with great coffee

Family comes first. Get up to 15 weeks of paid parental leave for the primary carer and up to 4 weeks for concurrent leave, so you can focus on what matters most

Stay Social & Connected.Join our Social Club and Open Committee for regular events, celebrations, and fun activities

Grow Without Limits.Access unlimited LinkedIn Learning courses and invest in your personal and professional development

The role

Reporting to the Head of Research, Analytics & Data Science, the MLOps Engineer will play a key role in helping our Data Science team deploy, operate and maintain machine learning and AI solutions in production.

Working closely with our Data Scientists, Data Engineers and Technology teams, you will help bridge the gap between model development and production by implementing reliable deployment, monitoring and operational processes.

The role will initially focus on the operationalising and ongoing support of our strategic machine learning engine hosted in Databricks, while also supporting a growing portfolio of predictive modelling, optimisation and AI use cases across the business.

What you’ll be doing

Partner with Data Science to take models from experimentation through validation, deployment and ongoing production management

Implement and maintain production workflows for data ingestion and processing, model execution, retraining, testing and deployment

Build and maintain CI/CD pipelines and controlled release processes for machine learning and AI workloads

Build and maintain robust data and feature pipelines required by machine learning and AI solutions

Diagnose production issues and work with Data Science and our Engineers to resolve model, data and platform problems

Implement appropriate access controls, security and governance within Databricks, including access and action permissions for AI agents and automated systems in line with our enterprise standards

Support operationalising of Generative AI solutions, including LLM applications, RAG and emerging AI use cases

Contribute to reusable templates, tooling and MLOps practices that make it easier to deploy new models consistently

Contribute to establishing best practice, configure and set up state of the art tooling to meet production standards

Support performance optimisation and efficient use of Databricks and cloud infrastructure

What’s in your toolkit?

You are a hands-on engineer with experience deploying and supporting data science and machine learning solutions in production environments. You enjoy working closely with Data Scientists and Engineers to turn analytical solutions in to reliable production workloads.

Strong Python and SQL skills, with experience using PySpark in distributed data environments

Experience with Azure Databricks and the Databricks ecosystem, including MLflow, Delta Lake, Unity Catalog and Workflows

Experience deploying, operating and monitoring machine learning models in production, including drift detection and performance/data quality alerting

Experience with CI/CD and software engineering practices for data or ML applications, ideally using Azure DevOps

Established skill set in setting up unit and integration testing frameworks in the context of AI/Machine learning projects

Experience with data processing performance optimisation and tuning

Understanding of governance/guardrails for AI agents, permissions, audit/traceability, operational risk

Experience in the following would be highly regarded:

Deploying and operationalising agentic AI and LLM-based solutions in production, including RAG, vector search, evaluation versioning and agent orchestration frameworks

AI agent management, tooling, MCP orchestration

Azure cloud services supporting AI and machine learning workloads

Databricks certifications or equivalent practical experience