Role details
Build production LLM and Generative AI applications for large enterprise clients across financial services, retail and government.
You'll join a specialist AI engineering squad designing, building and deploying production-grade Generative AI solutions end-to-end. Day-to-day that means working with LLMs, RAG architectures and foundation model fine-tuning, then integrating those capabilities into enterprise platforms via APIs and backend services. You'll also contribute to client workshops and solution design sessions, so you'll be across the full lifecycle from brief to deployment.
The team works hybrid with two days from home. The work itself spans multiple industries and clients, so you won't be stuck maintaining a single product for years. You'll be building alongside experienced AI engineers, data engineers and architects, with real projects rather than internal proof-of-concepts that never ship. The stack covers Python, PyTorch or TensorFlow, and cloud AI services across AWS Bedrock and SageMaker, Azure OpenAI, and Google Vertex AI.
The practice is growing fast, with multiple new AI squads forming as enterprise demand picks up. That means genuine room to move into senior and lead positions as the team scales. You'll be client-facing at a senior level, so if you want to stay heads-down with no stakeholder contact, this probably isn't the right fit. If you're comfortable in a room with clients and can translate a business problem into a technical solution, there's a clear path forward here.
What You'll Do
- Design, build and deploy production-ready Generative AI applications using LLMs, RAG and modern AI frameworks.
- Fine-tune and adapt foundation models for specific enterprise use cases across financial services, retail and government clients.
- Build APIs and backend services that integrate AI capabilities into existing enterprise platforms and architectures.
- Monitor and continuously improve deployed models for performance and reliability, contributing to solution design and client workshops.
What You'll Need
- 3 to 5 years' commercial software engineering or AI engineering experience, with a strong Python background and production application delivery.
- Hands-on experience with LLMs, prompt engineering, foundation model fine-tuning and Generative AI frameworks.
- Experience with cloud AI services across at least one of AWS (Bedrock, SageMaker), Azure AI / Azure OpenAI, or Google Vertex AI.
- Confidence working directly with clients and translating business requirements into technical AI solutions.
About the Company
They run one of Australia's fastest-growing Advanced AI engineering practices, delivering enterprise AI solutions across financial services, retail and government. Multiple new AI squads are forming as demand grows. The environment is technical, client-facing and focused on shipping production AI rather than internal experimentation.
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