Wesfarmers AI Engineer interview questions & guide 2026

The following questions reflect the technical rigor and strategic mindset required for this position. While specific inquiries may shift based on the team's current focus, you should prepare for a blend of deep technical architecture and practical problem-solving.

Generative AI and LLMs

This category focuses on your ability to work with modern transformer architectures, focusing on the infrastructure required to make them production-ready.

• How would you design a RAG pipeline to minimize hallucinations while maintaining low latency?
• What metrics would you prioritize when designing an LLM evaluation framework for a customer-facing chatbot?

Preparation for Wesfarmers requires a balance of deep technical mastery and a pragmatic, business-oriented approach. You should demonstrate that you can move beyond building prototypes and instead focus on creating sustainable, reliable infrastructure.

Technical Depth – You must be comfortable discussing the end-to-end lifecycle of an AI project. Interviewers look for your ability to explain why you chose a specific architecture or model over others, specifically focusing on trade-offs.

Systemic Thinking – The role requires you to consider the "big picture." You will be evaluated on how your code impacts the wider system, including latency, cost, and maintainability.

Communication Skills – You will often work with cross-functional teams. Being able to translate complex technical constraints into actionable business insights is a core competency.

Problem-Solving Agility – Be prepared to handle ambiguity. When presented with a vague scenario, structure your approach by asking clarifying questions before jumping into technical solutions.

The interview process at Wesfarmers is designed to assess both your engineering prowess and your ability to fit into a collaborative, results-driven culture. You can expect a structured journey that begins with a screening call to align on your background and the role’s requirements. Following this, the process typically moves into technical deep-dive rounds, which include both live coding and system design exercises.

The final stages involve behavioral interviews where you will discuss your experience working within teams and your approach to handling complex project challenges. The pace is professional and deliberate, reflecting the company’s emphasis on thoroughness and long-term value creation.

This visual timeline illustrates the typical progression from initial screening to technical and behavioral assessments. Candidates should use this to pace their study, ensuring they have sufficient time to refresh their knowledge of system design and core algorithmic concepts before the later rounds. Note that the specific number of technical rounds may vary based on the seniority of the role.

AI Infrastructure and LLMs

Success here depends on your understanding of the entire AI stack. You should be prepared to discuss how to move models from research to production.

• RAG pipeline design: Focus on retrieval accuracy, chunking strategies, and re-ranking.
• LLM evaluation: Be ready to talk about both automated metrics (like ROUGE or BERTScore) and human-in-the-loop evaluation.
• System design for LLM serving: Understand load balancing, GPU utilization, and caching strategies.

As an AI Engineer, your primary objective is to build and maintain the infrastructure that powers Wesfarmers' AI initiatives. You will be responsible for the full lifecycle of AI services, from data ingestion and model training to deployment and monitoring. A significant portion of your time will be spent optimizing pipelines to ensure they are scalable and cost-efficient.

Collaboration is key; you will work closely with data scientists to productionize their research and with software engineers to ensure seamless integration into existing customer-facing platforms. Your ability to maintain high standards for code quality, documentation, and system reliability will directly determine the success of the projects you lead.

A strong candidate for this role possesses a blend of high-level engineering skills and a deep interest in the application of artificial intelligence.

• Must-have skills:
  • Proficiency in Python and deep learning frameworks (PyTorch or TensorFlow).
  • Experience with cloud platforms (AWS, Azure, or GCP).
  • Strong understanding of data structures, algorithms, and system design principles.
  • Practical experience with vector databases and LLM orchestration (e.g., LangChain, LlamaIndex).
• Nice-to-have skills:
  • Experience with MLOps tools (Kubeflow, MLflow).
  • Knowledge of Kubernetes and container orchestration.
  • Familiarity with CI/CD pipelines for machine learning.

Q: How much preparation time is recommended? A: Most successful candidates dedicate at least 3–4 weeks of focused study, especially if they need to brush up on system design and recent advancements in generative AI.

Q: Is there a specific focus on coding language? A: Python is the primary language used for AI and data engineering at Wesfarmers, and you should be prepared to demonstrate high proficiency in it.

Q: What is the company culture like? A: The culture is collaborative, data-driven, and focused on long-term impact. You will be expected to be proactive and take ownership of your work.

Q: How is the role structured regarding remote work? A: These roles are typically hybrid, meaning you will be expected to spend time in the Melbourne office to foster team collaboration.

• Structure your answers: Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses focused and impactful.
• Be ready to trade-off: In system design, there is rarely one "right" answer. Always explain the trade-offs of your choices (e.g., latency vs. cost, consistency vs. availability).
• Deepen your knowledge: Stay updated on the latest research in multi-agent systems and RAG, as these are hot topics at Wesfarmers.
• Know the business: Familiarize yourself with the brands under the Wesfarmers umbrella, as this context will help you frame your answers in a way that shows business acumen.

The AI Engineer role at Wesfarmers is a unique opportunity to apply cutting-edge technology at a massive scale. By focusing on your technical foundations in LLMs, system design, and collaborative problem-solving, you will be well-positioned to succeed in your interviews. We encourage you to explore additional interview insights, practice questions, and preparation resources on Dataford to refine your approach.

The salary data provided represents the current market range for this position in Melbourne. Candidates should interpret these figures as a guide, noting that total compensation may vary based on experience, specific team requirements, and seniority level within the organization.