AI Engineer

Why this role exists: The AI Engineer is a pioneering role within a newly established AI capability, responsible for bridging the gap between traditional machine learning (ML) and modern artificial intelligence (AI). This role focuses on developing advanced AI features, including Large Language Model (LLM) implementations and agentic orchestration, to support Officeworks' transition toward a democratised, self-serve analytics model. As a member of a brand-new technology team, this role will build the foundational tools that allow the business to move from standard data reporting to intelligent, automated problem-solving.

Where you will make a difference: In this role you will: AI & LLM Development: Develop ML models and LLM features, including advanced prompting and Retrieval-Augmented Generation (RAG). Build basic agent flows, focusing on tool calls and the orchestration of intelligent agents. Support the leap from traditional ML to AI, adapting systems to handle new agentic architectures.

Drive agent orchestration to ensure complex AI tasks are executed efficiently and accurately. Data Engineering & Preprocessing: Create robust data pipelines and preprocessing workflows using BigQuery and Google Cloud Storage (GCS). Utilise Snowflake as the enterprise data platform for both AI and data-led initiatives.

Support the migration and integration of data from legacy systems like SAP BW into modern AI-ready environments. Quality, Deployment & Monitoring: Lead Quality Assurance and testing for AI solutions to ensure reliability and performance in production. Support the deployment, monitoring, and debugging of AI models to maintain high service stability.

Maintain structured, high-quality documentation of system architectures to ensure long-term sustainability within the team. Process & Continuous Improvement: Identify and execute process improvement opportunities within the AI development lifecycle to increase efficiency. Challenge existing workflows and mindsets to ensure technology solutions deliver maximum value with minimal manual execution.

Actively participate in the democratization of data, helping the business move toward self-service analytics. Who you will be working with: AI Technical Associate Manager: Partnering on the design and execution of agentic architectures. Analytics Hub: Collaborating with Data Architects, Modellers, and Cloud Engineers to align AI builds with the enterprise data strategy.

Leadership Teams: Coordinating with leadership teams to ensure delivery meets commercial requirements. Technology Specialists: Working with technical experts across disciplines to ensure seamless integration and platform stability. What success looks like: Feature Delivery: Successful implementation of RAG and LLM features that drive business performance.

System Stability: High availability of AI models through rigorous monitoring, debugging, and quality assurance. Sustainable Documentation: A robust library of documented technical knowledge that supports the long-term growth of the AI capability. Operational Flow: Efficient agent orchestration that reduces manual effort across business processes.

How you will lead: Individual Contributor: Lives our Officeworks values and behaviours Proactively contributes to a safe working environment, escalates appropriately if there are unsafe conditions or inappropriate behaviour Operates in line with applicable Officeworks company policies and Code of Conduct Demonstrates a strong sense of personal accountability and curiosity to learn and develop Qualifications and work experience: Essential: Education: Bachelors degree in Computer Science, Data Science, Mathematics, or a related field. Experience: 4+ years of experience in Machine Learning (ML) systems knowledge and data engineering. Adaptability: Demonstrated ability to understand and adapt to the transition from traditional ML to AI and adapt to new technology environments.

Technical Skills: Essential hands-on experience with GitHub, Snowflake, and ML systems. Cloud Proficiency: Experience building pipelines and models within GCP environments (BigQuery, GCS). Cultural Fit: High level of adaptability and a self-starter mindset is valued.

Preferred: Advanced AI: Experience with agent orchestration, RAG, and prompt engineering. Agile Delivery: Experience working in cross-functional squads with a focus on iterative improvement.