AI Platform Frontend Engineer

Betashares; Sydney; On-site; AUD 110,000 - 170,000; Full time

Job summary

Betashares is hiring a Software Engineer to build our internal AI Platform, powering Agentic AI use cases across the organisation. This frontend-heavy role focuses on crafting intuitive interfaces while also bringing backend competencies for platform services and integrations.

You’ll work with designers, business users, and platform engineers to deliver AI-native experiences company-wide, with hands-on execution and a bias toward continuous improvement across our AI ecosystem.

Qualifications

• Strong frontend engineering fundamentals with TypeScript/React techniques.
• Experience with AI tooling and patterns in development workflows.
• Experience with Agentic AI: LLM apps, multi-agent patterns, tool use or AI orchestration.
• Ability to translate ambiguous business requirements into concrete UI specs.
• Comfort across UI polish and platform architecture decisions.

Responsibilities

• Design and build AI interfaces and interaction patterns for the internal platform and business applications.
• Own frontend architecture for AI-powered features and contribute to backend APIs and integrations.
• Collaborate with designers to establish UI/UX standards for complex AI interactions.
• Prototype interfaces for AI use cases and iterate with business users.
• Connect frontend experiences with backend AI infrastructure and tools.
• Test AI features and optimize user experience for staff.

Skills

TypeScript/React

AI tooling experience

Agentic AI

UX collaboration

Full-stack familiarity

Job description

Betashares is hiring a Software Engineer to build our internal AI Platform, powering Agentic AI use cases across the organisation. This frontend-heavy role focuses on crafting intuitive interfaces while also bringing backend competencies for platform services and integrations.

You’ll work with designers, business users, and platform engineers to deliver AI-native experiences company-wide, with hands-on execution and a bias toward continuous improvement across our AI ecosystem.