The posting
Key Responsibilities
- Develop AI/ML models for business applications. Depending on project needs and your expertise, contribute to one or more areas including search and recommendation, marketing decision-making, demand and replenishment forecasting, logistics optimisation, and AI agents.
- Build data and feature processing pipelines. Develop extraction, cleaning, transformation, loading and feature generation processes for user behaviour, product descriptions and attributes, orders, and fulfilment records. Work with structured, semi-structured and unstructured data to support batch and real-time or near-real-time model applications.
- Deploy models into production. Train and tune models, implement inference workflows, and integrate models into scalable, maintainable services or batch scoring processes. Connect these solutions with the company’s software platforms, data infrastructure and business systems.
- Design experiments and evaluate outcomes. Conduct offline evaluations, backtesting and online A/B experiments to assess model performance and business impact. Investigate data quality, feature effectiveness and evaluation bias, and use findings to guide further development.
- Maintain and optimise deployed models. Monitor model quality, changes in data and features, inference latency, throughput and computing costs. Diagnose and resolve issues in models, data pipelines and deployed services to improve reliability and efficiency.
- Collaborate across functions. Work with product, operations, supply chain, logistics and software engineering teams to translate business needs and constraints into technical solutions and acceptance criteria. Take responsibility for development, testing, deployment, documentation and model outcomes.
- Evaluate relevant AI technologies. Assess developments in machine learning, deep learning and large language models, and build practical prototypes to identify solutions that deliver measurable value within realistic cost and operational constraints.
Requirements
- Bachelor’s degree or above in Computer Science, Information Technology, Artificial Intelligence, Mathematics, Statistics, or a related discipline, with at least 3 years of professional experience in AI or machine learning engineering and the ability to independently develop, evaluate and deploy models into production.
- At least one year of professional experience in AI, machine learning or algorithm development. Candidates qualifying through the work experience pathway above must meet the three-year AI Engineer experience requirement.
- Strong foundations in statistics, machine learning and algorithms, with an understanding of model training, validation, generalization and inference. Practical depth in at least one of the following: search and recommendation, marketing algorithms, demand forecasting and logistics optimisation, or LLMs and AI agents.
- Proficiency in Python and SQL, experience with at least one deep learning framework such as PyTorch or TensorFlow, and the ability to process data and develop features using tools such as Pandas or Spark.
- Ability to integrate models into API services or batch processing workflows, with an understanding of model versioning, deployment, monitoring and troubleshooting.
- Strong analytical and problem-solving skills, with the ability to formulate hypotheses and validate them through experiments. A responsible, self-driven approach and effective communication and teamwork skills.
Preferred Qualifications
- Experience delivering AI/ML projects across solution design, data preparation, development, deployment and evaluation, with demonstrable business results.
- Experience in e-commerce, fresh food retail, B2B procurement, supply chain or logistics.
- Hands-on experience with LLM fine-tuning, retrieval-augmented generation (RAG), AI agents, tool integration or model evaluation.
- Backend or full-stack development experience, with the ability to integrate models, APIs and business applications.
