Sydney AI Engineering & Infrastructure Summit 2025

An exploration of the complexities involved in engineering scalable AI systems, including data pipelines, model deployment, and real-time processing.

Discover a proven roadmap for building and deploying enterprise AI solutions that deliver real business value. Explore the essential steps, from strategic planning and resource alignment to streamlined development workflows, that ensure future growth and adaptability.

‍

This panel discussion delves into the compelling reasons to modernise AI, outlines sophisticated implementation approaches, and identifies the essential stakeholders who can champion a truly transformative agenda.

• Examining the drivers prompting organisations to transformation applications and systems
• Outlining advanced strategies for refining infrastructure, data pipelines, and MLOps processes to scale AI effectively
• Highlighting cross-functional roles necessary to orchestrate AI modernisation successfully
• Fostering collaboration, robust governance, and continuous learning to ensure AI’s long-term viability and impact

In this innovative session, attendees will be faced with a series of scenarios that they may face in their roles. Attendees will discuss the possible courses of action with their peers to consider the ramifications of each option before logging their own course of action. 

Results will be tallied and analysed by our session facilitator and results will impact the way the group moves through the activity.

Will we collectively choose the right course of action?

‍

As one of Australia’s leading employment marketplaces, SEEK leverages AI to match job seekers with the right opportunities and assist employers in finding top talent. This session unveils how SEEK integrates responsible AI principles into its suite of products and programs—ensuring transparency, fairness, and trust across its extensive ecosystem of users and partners.

‍

A leading AI engineer shares how they consolidated disparate data pipelines into a unified platform, reducing complexity and improving model accuracy. Attendees will learn actionable strategies for orchestrating data flows and ensuring data quality.

• Centralising siloed data sources to streamline AI workflows
• Automating data ingestion and transformation for consistent inputs
• Enhancing collaboration between data and engineering teams

‍

Explore the technical intricacies of designing, deploying, and scaling AI infrastructure. Delve into the tools, frameworks, and architectures that power high-performance AI solutions, and learn how to balance agility, security, and cost-efficiency.

• How do teams architect resilient, high-performance computing environments to support AI workloads at scale?
• How can teams ensure real-time, high-volume data flow for AI?
• Which pipelines streamline model development, deployment, and continuous monitoring?

‍

Roundtable topics to be shared with registered attendees for their selection

At the intersection of compliance, technology, and organizational culture, sendpayments.com has achieved a 40% improvement in engineering velocity by treating AI agents like human staff. Operating in a highly regulated industry, their strategy involves thoroughly vetting AI through policy and governance committees before any workflow implementation.

• Defining, Onboarding, and Reviewing AI Roles: Creating AI “job descriptions,” profiling responsibilities, and conducting performance evaluations as if AI were full-fledged team members
• Putting Tasks Before Technology: Ensuring AI augments rather than dictates workflows by focusing first on business requirements and operational needs
• Institutionalising Governance: Feeding every AI decision through policy and governance committees to uphold trust, transparency, and regulatory alignment

‍

‍

In this interactive session, participants will explore and debate five hot-button issues shaping AI’s future. Expect divergent views and lively discussion on how these trends could redefine both engineering practices and business outcomes.

• Cloud vs. On-Prem High-Performance Computing – Balancing elasticity, control, and cost
• AutoML Tools – Do they empower teams or oversimplify complex engineering challenges?
• Ethical AI vs. Speed to Market – Should organisations slow innovation to ensure responsible development?
• Edge AI vs. Centralised Processing – Is pushing more AI to the edge truly efficient or overly complex?
• Low-Code/No-Code AI – Does democratising AI risk quality and governance, or is it the key to widespread adoption?

‍