AI Engineer – 9 Month Fixed-term Contract
ANZ
contract
Posted on:
Location Type: Hybrid
Location: Melbourne • Australia
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About the role
- Collaborating to support the achievement of project or business outcomes such as process mapping and change transition as well as the technical implementation of the data and AI processes.
- Working alongside Teams in Customer Resolutions, Data Portfolio, ANZ*Plus, Assurance as well as key support partners in Risk, Technology and Operations.
- Actively participating in feedback processes to develop and refine solutions leveraging large language models.
- Establishing the analytic development process to continuously refine these models and deploy new models/model versions in a controlled and measured way, including establishing best practices for change management.
- Addressing and solving complex business issues using large amounts of data (structured and unstructured).
- Understanding the application of Generative AI in a cloud data & analytic eco-system.
- Designing, building, deploying, monitoring, and assessing relevant models for implementation as AI Agents in the complaints management value chain.
- Identifying data from a variety of sources to combine, synthesize and analyze to support complaints quality management processes.
Requirements
- Good understanding of the Banking system and products, service, channels in support of the understanding of the customer experience that led to the Complaint
- Highly desirable to have good knowledge of Complaints management processes, Quality Assurance processes and the relevant regulatory environment.
- Desirable to have good domain knowledge of ANZ systems
- Strong customer lens with a bias towards safety, consistency and fairness in how AI is applied to the process.
- Solid understanding of predictive modelling, pattern recognition, clustering, supervised and unsupervised learning
- Proven experience in applied probability and statistics
- Hands-on knowledge and experience with tools and techniques for analysis, data manipulation and presentation
- Experience in using open source technologies for Data Science, specifically Python and key supporting packages/libraries and development environments
- Experience in using Generative AI to solve real business challenges, ideally with multiple model families (e.g. Gemini, Anthropic, OpenAI, Meta)
- Experience with Retrieval Augmented Generation (RAG) frameworks, and best practices for generating and using embeddings.
- Use of Google Cloud Platform services such as Vertex AI and related data management services (BigQuery, CloudSQL etc).
- Ability to understand and test trade-off for model quality, performance and cost management
- Strong ability to translate model outputs and insights into practical business recommendations
- Ability to effectively communicate and present to all stakeholders (technical and non-technical)
- Data Collection and Cleaning - Leads teams in identifying data sources for large projects; extensively knowledgeable in best-practice data cleaning techniques and handling complex data issues.
- Feature Engineering - Masters a wide range of techniques and optimizes feature engineering processes.
- Machine Learning Modelling - Selects appropriate modeling techniques, translates business requirements, and systematically improves model performance.
- Model Evaluation and Validation - Ability to assess and monitor the performance of AI solutions, and assess and address drivers of poor performance.
- Data Visualisation - Draws insights from data, supports decision making, and automates data visualization.
- Soft Skills: Communication, Problem Solving, Planning & Prioritisation, Critical thinking.
Benefits
- access to health and wellbeing services
- discounts on selected products and services from ANZ
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
predictive modellingpattern recognitionclusteringsupervised learningunsupervised learningapplied probabilitystatisticsdata manipulationfeature engineeringmachine learning modelling
Soft skills
communicationproblem solvingplanningprioritisationcritical thinkingcustomer focuscollaborationfeedback participationanalytical thinkingstakeholder presentation