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.
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 frameworks
- Building and maintaining the infrastructure behind it: vector databases, embedding pipelines, model serving and monitoring
- Managing 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/CD
- Setting engineering standards for how AI gets built across the business, including guardrails, testing and responsible use
- Working closely with product managers, data engineers and stakeholders to take ideas from prototype to production
- Mentoring 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 work
- Hands-on experience building with LLMs: RAG, prompt engineering, fine tuning, model APIs and evals
- Solid 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 systems
- The ability to talk trade-offs with non-technical stakeholders and push back when something shouldn't be built
- Full working rights in Australia
Nice to have - Experience with LangChain, LlamaIndex or similar frameworks
- Classical ML background (scikit-learn, PyTorch, TensorFlow)
- Exposure to MLOps tooling like MLflow, SageMaker or Azure ML
- Experience in a scale-up or enterprise environment where you've built AI capability from early days