Founding AI Engineer

Change Recruitment; Sydney; Hybrid; AUD 180,000 - 230,000; Full time

Benefits offered by this job

Meaningful equity

Sydney-based (hybrid)

Small team, high ownership

Job summary

Change Recruitment is seeking a hands-on engineer to own the AI platform that backs a growing set of businesses. This role emphasizes production-ready systems, with a focus on reliability, scalability, and end-to-end ownership.

You'll design multi-agent workflows, RAG pipelines, knowledge graphs, and eval frameworks, shipping capabilities used by real users. Sydney-based hybrid work, meaningful equity, and a small, high-ownership team await.

Qualifications

• Experience shipping LLM-powered products in production.
• Hands-on with agents, RAG, evals, tool use, structured outputs.
• Understanding context management, retrieval quality, and system evaluation.
• Experience building production-grade architectures and APIs.

Responsibilities

• Build multi-agent systems and production-grade orchestration layers.
• Design RAG pipelines and retrieval infrastructure.
• Create knowledge graphs and context management systems.
• Develop eval frameworks and testing environments.
• Connect models to internal tools, databases, APIs, and workflows.
• Improve latency, reliability, observability, and cost efficiency.
• Ship production systems and learn from usage.

Skills

LLM-powered products

Agents and RAG

Eval frameworks

Software engineering

Cloud infrastructure

Tools

Kubernetes

CI/CD pipelines

Cloud platforms (AWS, GCP)

APIs design

Job description

You'll own the AI platform behind a growing set of businesses. The goal isn't to build demos, copilots, or internal tools. It's to build systems that can take on meaningful operational work and get better over time.

That means designing multi-agent workflows, building RAG systems that people can actually trust, creating knowledge graphs, running evals, and figuring out how to make all of it reliable in production. Some of the work is greenfield. Some of it is untangling messy real-world processes and turning them into software.

You'll spend a lot of time talking to the people closest to the work. Understanding how decisions get made, where information lives, what breaks, and what can be automated. Then you'll build systems around it.

One day you might be improving retrieval quality across millions of documents. The next you could be designing agent workflows that coordinate multiple tools and systems, building evaluation frameworks, connecting models to internal systems, or debugging why something that worked perfectly in testing falls apart in production.

This isn't a research role. The expectation is that you ship. Quickly. The feedback loops are short and the impact is obvious.

What you'll be doing

• Building and deploying multi-agent systems.
• Designing RAG pipelines and retrieval infrastructure.
• Building knowledge graphs and context management systems.
• Creating eval frameworks and testing environments.
• Connecting models to internal tools, databases, APIs, and workflows.
• Building orchestration layers that coordinate models, tools, and business systems.
• Improving latency, reliability, observability, and cost efficiency.
• Turning one-off solutions into reusable platform capabilities.
• Shipping production systems and learning from how they're actually used.
• Working directly with domain experts to understand workflows and translate them into software.

What they're looking for

• You've built and shipped LLM-powered products into production.
• You have hands-on experience with agents, RAG, evals, tool use, structured outputs, and modern AI frameworks.
• You understand the challenges of context management, retrieval quality, agent reliability, and system evaluation.
• You've built systems that real users depend on, not just prototypes or internal experiments.
• You're a strong software engineer first and comfortable owning systems end-to-end.
• You can move between backend services, data infrastructure, cloud infrastructure, and user-facing applications.
• You're comfortable designing APIs, data models, and production architectures from scratch.
• You've worked in startup, founding, or high-ownership environments where speed matters.
• You care about observability, testing, reliability, and performance as much as model quality.
• Experience with AWS, GCP, Kubernetes, CI/CD pipelines, or modern cloud infrastructure is highly regarded.
• Experience building systems across multiple models, providers, and toolchains is a plus.
• You're comfortable making technical decisions without perfect information and figuring things out as you go.

Package

• $180k-$230k base
• Meaningful equity
• Sydney-based (hybrid)
• Small team, high ownership

Don't worry if your CV isn't perfectly up to date. We'd rather speak with great engineers than wait for the perfect application.