ML Engineer

Job Description

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.

What you’ll do:

• 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
  
  

What you’ll need:

• 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
  

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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