Summary
✨ AI‑Generated
A globally established financial services organization is expanding its AI engineering function and is seeking a Senior AI Engineer focused on LLM and Generative AI. You will design and ship production LLM features, including RAG pipelines, agentic workflows, and evaluation frameworks, while owning solutions from initial use-case assessment through deployment and ongoing production operation. The role is permanent, full-time, and hybrid.
Highlights
Newly created senior role with strong organizational backing, dedicated investment, and responsibility for production-scale LLM and Generative AI solutions. The position provides substantial influence over architecture and end-to-end delivery, from identifying suitable AI use cases through deployment and production operations.
Description
We're working with a globally recognised financial services business looking to build on their AI function.
They're well past the experiment stage, with executive backing, a growing engineering team and real budget behind the roadmap.
The systems this team builds go into production and get used at serious scale, not parked in a demo environment.
This is a newly created senior role, so you'll have a real say in how things get built rather than inheriting someone else's decisions.
Sydney CBD, hybrid (3 days in office)
$160,000 to $200,000 + super
Permanent, full time
You'll be the senior hands-on engineer for their LLM and GenAI work.
That means owning solutions end to end, from working out whether a problem actually needs AI through to design, build, deployment and keeping it running well in production.
Day to day you'll be:
Designing and shipping LLM powered features into production, including RAG pipelines, agentic workflows and evaluation frameworksBuilding and maintaining the infrastructure behind it: vector databases, embedding pipelines, model serving and monitoringManaging cost, latency and quality trade-offs across model providers (OpenAI, Anthropic, open source)Writing production Python and deploying on AWS or Azure with proper CI/CDSetting engineering standards for how AI gets built across the business, including guardrails, testing and responsible useWorking closely with product managers, data engineers and stakeholders to take ideas from prototype to productionMentoring mid-level engineers as the team grows
What you'll bring
Strong software engineering fundamentals with 5+ years experience, including recent production AI or ML workHands-on experience building with LLMs: RAG, prompt engineering, fine tuning, model APIs and evalsSolid Python and experience with cloud platforms (AWS or Azure)Experience getting models into production, not just notebooks.
You know what monitoring, versioning and rollback look like for AI systemsThe ability to talk trade-offs with non-technical stakeholders and push back when something shouldn't be builtFull working rights in Australia
Nice to have
Experience with LangChain, LlamaIndex or similar frameworksClassical ML background (scikit-learn, PyTorch, TensorFlow)Exposure to MLOps tooling like MLflow, SageMaker or Azure MLExperience in a scale-up or enterprise environment where you've built AI capability from early days
