ML Engineer

Kogan.com is a pioneer of Australian eCommerce, and the software we build is used by millions of customers every day. You'll join a fast-moving engineering team with real ownership, shipping to production daily and using AI as part of how we work.

 

As a Data Engineer you'll design and run the data and ML pipelines that let teams across Marketing, Purchasing, Logistics and Finance make confident, data-driven decisions.

Machine Learning Development: Design, build and deploy machine learning models that solve practical business problems, including recommendation systems, demand forecasting, customer segmentation, churn prediction, pricing optimisation and fraud detection.

Production ML Systems: Build reliable and scalable machine learning services that deliver predictions in both real time and batch environments, ensuring strong performance, reliability and cost efficiency.

MLOps & Automation: Develop and maintain MLOps pipelines that automate model training, validation, deployment, monitoring and retraining.

Feature Engineering: Work with large datasets to create robust feature pipelines and reusable datasets that improve model performance and accelerate experimentation.

LLM & Generative AI Applications: Design, evaluate and deploy AI powered solutions using large language models (LLMs), retrieval systems, agents and emerging AI technologies to enhance customer experiences and internal productivity.

Model Monitoring & Optimisation: Implement monitoring frameworks to track model performance, drift, accuracy and business impact, continuously improving models in production.

Cross Functional Collaboration: Partner with Product Managers, Engineers, Analysts and business stakeholders to identify opportunities where machine learning can create measurable value.

Software Engineering Excellence: Develop solutions in line with software engineering best practices, including Git, CI/CD, trunk based development, testing and observability.

AI Collaboration: Contribute to experiments with AI and emerging technologies, helping shape how Kogan.com leverages machine learning and automation across the business.

• Strong Python Skills: Commercial experience building machine learning applications and data pipelines using Python and relevant machine learning libraries.
• Machine Learning Expertise: Hands on experience developing, evaluating and deploying machine learning models in production environments.
• MLOps Experience: Experience building and maintaining model deployment, monitoring and retraining pipelines using modern MLOps practices and tools.
• Data Engineering Foundations: Strong SQL skills and experience working with large datasets, feature engineering workflows and distributed data processing systems.
• Cloud Experience: Hands on experience with cloud platforms, preferably Google Cloud Platform (GCP), including Vertex AI, BigQuery and Cloud Run.
• Software Engineering Best Practices: Strong understanding of Git, CI/CD, testing frameworks, containerisation and production system reliability.
• AI & LLM Experience: Experience working with LLMs, embeddings, vector databases, RAG architectures or AI agents through commercial or personal projects.
• Problem Solving Mindset: A practical engineering approach that balances experimentation and innovation with reliability, scalability and business impact.

To find out more about how we work, our tech stack, processes and company culture, visit https://careers.kogan.com and https://devblog.kogan.com