ML Engineer

• 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.

Bonus Points

• Recommendation & Forecasting Systems: Experience building recommendation systems, search ranking models or forecasting solutions.
• ML Platforms & Tooling: Exposure to ML platforms and tools such as Vertex AI, Databricks, SageMaker or Kubeflow.
• Streaming & Event Driven Systems: Experience with event driven architectures and streaming technologies such as Kafka or Pub/Sub.
• LLM Application Deployment: Experience deploying AI applications powered by LLMs and agent frameworks.