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About the company: My client is a leading and Australian bank headquartered in Sydney. The bank has a portfolio of financial services brands and businesses and provides a broad range of banking and financial services in the Australian market. They have strong customer facing divisions around consumer, banking and institution banking. About the role: Because of their immense growth in their portfolio in the big data platform, multiple exciting opportunities have come up in one of their offices in Sydney. Are you a skilled AI practitioner who thrives on moving beyond simple experimentation to deliver true, production-grade business outcomes? Join a growing enterprise AI Centre of Excellence and work at the frontier of applied AI. As a senior technical authority embedded within product and domain squads, you will shape the architecture, evaluation methodology, and technical culture of the AI function. This delivery-focused role is perfect for professionals who want to combine deep technical craft with enterprise-level impact, delivering advanced AI capabilities (LLMs, agentic systems) that are safe, repeatable, and customer-obsessed. What You Will Do: Develop core components of agentic AI products, including agent reasoning loops, tool use interfaces, memory systems, and orchestration logic. Translate architectural designs into robust, tested, production-grade implementations across data science and software engineering boundaries. Design and implement prompting strategies, retrieval pipelines, and context assembly to determine high-quality agent behavior. Embed rigorous evaluation strategies for probabilistic systems, including offline evaluations and online experimentation. Implement strong observability across AI systems, including behavioral drift detection, anomaly monitoring, and incident classification.
Job Responsibility
Develop core components of agentic AI products, including agent reasoning loops, tool use interfaces, memory systems, and orchestration logic
Translate architectural designs into robust, tested, production-grade implementations across data science and software engineering boundaries
Design and implement prompting strategies, retrieval pipelines, and context assembly to determine high-quality agent behavior
Embed rigorous evaluation strategies for probabilistic systems, including offline evaluations and online experimentation
Implement strong observability across AI systems, including behavioral drift detection, anomaly monitoring, and incident classification
Requirements
Strong experience delivering applied AI/ML systems into production, with exposure to LLMs and agentic architectures preferred
Hands-on expertise in building automated evaluation pipelines, runtime controls, and CI/CD integration for AI systems
Strong foundations in statistics, probability, and experimental design to design rigorous experiments and quantify uncertainty
Familiarity with prompt and context engineering, retrieval-augmented generation (RAG), tool use patterns, and agent orchestration
Experience operating in cloud-native environments and collaborating in cross-functional product, engineering, and risk squads