AI Engineer

2 weeks ago

Melbourne, Victoria, Australia ANZ Full time $90,000 - $120,000 per year
About UsAbout the Role
  • Address and solve complex business issues using large amounts of data (structured and unstructured)
  • Understand the application of Generative AI in a cloud data & analytic eco-system, and develop analytical solutions that best support transformation of the Complaints quality business processes
  • Continuous development, monitoring, presenting and conducting of 'fact-based' analysis and models to use at any time for the development and improvement of relevant quality management processes
  • Design, build, deploy, monitor and assess relevant models for implementation as AI Agents in the complaints management value chain (identify, classify, manage, quality control, remediate)
  • Design and implement best practices for Agentic AI solutions to effectively operate in an environment comprised of other heterogeneous agents with access to a diverse set of tools.
  • Identify data from a variety of sources to combine, synthesise and analyse to support complaints quality management processes
What will you bring?
  • 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.
  • 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
  • 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.

ANZ

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