Melbourne AI Engineering & Infrastructure Summit 2025

In the rush to implement Large Language Models, many organisations are overlooking the strategic value of traditional machine learning approaches. Through real-world examples and practical frameworks, this talk challenges the "LLM everywhere" mindset and demonstrates how a hybrid approach combining targeted traditional ML with LLMs can create more reliable, cost-effective, and safer AI systems.

How do you architect an AI-native platform purpose-built for vector search, LLM workflows, and scale? In this technical session, Relevance.ai Co-Founder Daniel Palmer shares the foundational decisions and cutting-edge infrastructure behind the platform's rapid growth. Gain insights into the real engineering behind enabling powerful, scalable, and enterprise-ready AI capabilities.

• Designing for Scale from Day One: How Relevance.ai architected a vector-first platform to support fast, scalable AI-driven use cases
• Compute and Cost Efficiency at Scale: Balancing performance, latency, and cost using smart infrastructure strategies across multi-cloud environments
• LLM Workflow Orchestration: Powering custom enterprise AI workflows with modular, flexible pipelines and real-time data processing
• From MVP to Enterprise-Ready: Evolving the platform to meet enterprise security, reliability, and integration requirements without sacrificing speed

This panel discussion dives into the compelling reasons to leverage AI when modernising tech estates and businesses, 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
• Highlighting key opportunities including using AI to help modernise, and as a part of the modernised state
• Understanding cross-functional roles necessary to orchestrate AI enabled modernisation successfully
• Outlining advanced strategies for refining infrastructure, data pipelines, and MLOps processes to scale AI effectively
• 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?

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

Creating AI-powered products and AI-assisted modernisation demand innovation—but too often, AI initiatives stall because teams and organisations struggle to manage the uncertainty that is inherent when innovating with AI and machine learning.

In this session, we’ll share how we bring clarity and momentum to AI projects by:

• Managing AI initiatives like R&D projects, grounded in data science principles
  
• Accelerating test-and-learn cycles with practical, adaptive strategies
  
• Using decision-making frameworks that align investment with confidence levels

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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.

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?

Delegates will chose from a list of pertinent peer-to-peer discussion topics focussing on evolving and emerging trends, techniques and technologies

Battle it out in a quiz that mixes AI, data, business, pop culture, and a sprinkle of nostalgia—expect big laughs, fierce competition, and ultimate bragging rights!

Join us for an exclusive look at how Canva is transforming the design experience through artificial intelligence. In this session, we’ll dive into the company’s AI journey, exploring how they’re using AI to empower users, enhance creativity, and scale their platform to millions globally.

• The integration of AI into Canva’s design platform and its impact on user creativity
• The technological infrastructure and challenges behind scaling AI solutions at global scale
• The future of AI at Canva: Innovations and upcoming trends shaping the platform

Learn how New Aim unified DevOps, DataOps, and MLOps into a single operational framework to accelerate AI development and deployment. This session explores the architecture and workflows enabling fast, scalable AI solutions across Australia’s fastest-growing e-commerce company.

• How Unified Ops enhances AI velocity by merging platform, data, and ML operations
• Inside the architecture: GitOps workflows powering LLMs and forecasting models at scale
• Balancing simplicity and scale to drive low-maintenance, high-impact engineering outcomes

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Join an interactive discussion where you’ll vote, debate, and dissect five of the thorniest challenges facing AI engineering today. Each topic will be explored through live multiple-choice questions designed to split the room and ignite fresh thinking across tech, data, and business leaders.

1. What's the biggest blocker to scaling AI across the enterprise?
2. Who should own AI infrastructure decisions in an organisation?
3. What’s the smartest way to modernise legacy systems for AI readiness?
4. How much automation is too much in AI development?
5. What matters most for long-term AI success?

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