Full Stack AI Engineer

Latitude IT; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Latitude IT seeks a software engineer to apply AI in production across a fast-growing Australian marketplace. You’ll own end-to-end delivery of production-grade AI agents and scalable services, shaping architecture and reusable patterns.

You’ll work with a hands-on CTO, integrate LLMs and external APIs, implement guardrails, testing and observability, and relentlessly improve reliability and performance across customer interactions and internal workflows.

Qualifications

• Proven track record shipping production AI apps.
• Experience designing scalable architectures for AI-driven products.
• Ability to communicate across technical and non-technical audiences.
• Ownership from discovery through production and iteration.

Responsibilities

• Designing, building and deploying production AI agents across customer interactions, automation and internal workflows
• Building scalable full-stack applications in TypeScript, Node.js, React and AWS, integrating LLMs and external APIs
• Creating reusable components and guardrails to speed AI delivery across the business
• Owning technical design across architecture, integrations, reliability, security and performance
• Collaborating with product, engineering and business teams to identify commercial AI value
• Setting testing, monitoring and maintenance standards for AI in production

Skills

Ownership

Ambiguity tolerance

Communication

Cross-team collaboration

Customer impact awareness

Tools

TypeScript

Node.js

React

AWS

LLM orchestration

Multi-agent systems

Guardrails

Observability

Tool calling

Job description

There's a big difference between building an AI demo and making it work reliably in production. That's where this role comes in.

You’ll join an Australian digital marketplace that’s scaled rapidly over the past few years and is now putting AI to work across its customer-facing products and internal operations. There are live agents delivering measurable results, a clear strategy for where AI can add value, and plenty more to build following significant investment in the underlying technology platform.

The engineering team is relatively lean, which means you'll have genuine ownership of what you build and the technical decisions behind it. You'll work closely with a hands-on CTO who’s leading the AI strategy, helping shape the architecture, engineering standards and reusable frameworks that will support future development.

At its core, this is a software engineering role with a strong focus on applied AI. Building with LLMs is one thing. Making those systems reliable, scalable and maintainable in production is where the real engineering challenge lies.

You'll be responsible for:

• Designing, building and deploying production AI agents across customer interactions, automation and internal workflows
• Building scalable full‑stack applications in TypeScript, Node.js, React and AWS, integrating LLMs, external APIs and agentic frameworks
• Creating reusable components, orchestration patterns and guardrails that speed up AI delivery across the business
• Owning technical design across architecture, integrations, reliability, security and performance
• Working with product, engineering and business teams to find where AI pays off commercially
• Setting the standard for testing, evaluating, monitoring and maintaining AI in production

What you bring:

• Deep commercial experience building production software in TypeScript, Node.js, React and AWS
• LLM-powered or agentic applications shipped to production, not left at proof of concept
• Solid system design: APIs, integrations, scalability and the trade-offs that come with them
• Ownership from discovery and design through to production and ongoing improvement
• A clear view of how engineering choices affect customers, operations and the bottom line
• Confidence to challenge assumptions, work through ambiguity and talk to technical and non-technical people alike
• LLM orchestration, tool calling and multi-agent systems
• RAG and evaluation frameworks
• Observability for AI applications in production