Introduction

Most AI roles are a proof of concept that dies in a slide deck. This one is not.

Learning Online Group is an online education company operating across Australia, Canada, the UK and New Zealand. Over 100 staff, a portfolio of brands spanning beauty, photography, animal care and tattooing, and a growing amount of the business quietly running on systems our AI function built. We already have a South African team working alongside our Australian operation, and we are growing it.

This is not aspirational. We already have LLM-powered document automation, AI-assisted grading and chatbots in production around Salesforce, n8n and Postgres. What comes next is planned and funded: a Google Cloud data platform on BigQuery, and a multi-agent orchestration layer powering tutoring, student service and analytics agents across every brand.

Somebody has to build that. We are hoping it is you.

One thing to be clear about upfront: this is a fully remote role working with our Australian team, which means an early start in South Africa. Be honest with yourself about that before you apply, because the hours are the part people underestimate.

Duties & Responsibilities

You are an engineer first. Not research, not data science. You write tested, typed Python, ship behind CI/CD, and own what you run in production. When it breaks you are the one reading the logs.

You will work across Google, Salesforce, Vonage, Customer.io and WordPress, connected through n8n and custom apps, and increasingly through AI agents and tools like Claude Code and Antigravity.

Depending on where you land in the range, the job looks like this:

- Build and operate production LLM applications: RAG over governed knowledge bases, tool and function calling,



structured outputs, and multi-agent orchestration where a supervisor agent coordinates specialists
- Own evaluation and observability: golden datasets, automated evals for non-deterministic behaviour, tracing, cost and latency monitoring. If you cannot prove it got better, it did not
- Build integrations and semantic data layers across Salesforce, our LMS and finance platforms, with event-driven automation and APIs that other people can actually use
- Stand up our Google Cloud AI stack: BigQuery, Cloud Run services in Python (FastAPI), vector search, and the Claude, OpenAI and Gemini APIs
- Implement guardrails and responsible AI controls: least-privilege access, prompt-injection defence, privacy-safe data handling
- Monitor production, triage failures, and keep the runbooks honest

The first six months are already mapped: take ownership of the live automation estate, ship internally scoped features, take our internal knowledge assistant from design to production in Slack, and lay the BigQuery foundations for the agent roadmap.

Desired Experience & Qualification

We have deliberately advertised one role with a wide band, because we would rather meet valuable people than argue about titles. Where you land depends on what you have actually shipped.

To be in the conversation at all:

- Solid Python, with Git,



and enough SQL to check your own results
- You have called REST APIs and handled JSON and webhooks in something you built
- You have built at least one thing with an LLM API and can explain, in detail, where it breaks. Personal projects count. Hackathons count. "I read about RAG" does not
- A degree in computer science, software engineering, AI or data, or the equivalent proven in work

To be at the top of the band:

- 5+ years software engineering, including 2+ years running LLM or agentic systems in production, where "production" means real users and real consequences
- Hands-on RAG: embeddings, vector databases, retrieval design, and the evals that prove quality
- Enterprise integration depth: REST, webhooks, OAuth or JWT. Salesforce experience is a real advantage
- Cloud engineering with Docker and CI/CD. Google Cloud preferred, AWS or Azure welcome
- You have set engineering standards for other people and are happy mentoring a less experienced engineer
- You can sit with a non-technical department head in another country, work out what they actually need, and translate it into a system

Nice to have at either end: multi-agent frameworks (LangGraph, CrewAI), Model Context Protocol, FastAPI, education sector experience, and previous remote work with an international team.

Some of our strongest applicants come from South African software houses and product teams (Entelect, BBD, DVT, Synthesis, Dariel), local scale-ups and fintechs (Takealot, Yoco, Luno, JUMO, Prodigy Finance, Lula), data and engineering teams inside the big insurers and banks (Discovery, Old Mutual, Standard Bank, Momentum), or from remote con

📌 AI Engineer (Australia)
🏢 Learning Online Pty
📍 Australia