Responsibilities
Design, build, and ship production-ready AI applications, services, agents, and internal tools.
Integrate foundation models, ML models, RAG, vector stores, and AI services into secure enterprise workflows.
Build pipelines for prompt management, model invocation, grounding, validation, and evaluation.
Embed security, privacy, compliance, model-risk, and responsible-AI guardrails throughout delivery.
Build testing, evaluation, monitoring, and feedback loops for quality, performance, safety, and cost.
Optimise latency, reliability, and cost across AI workloads.
Collaborate with product, data, risk, security, and platform teams to clarify requirements, manage trade-offs, and prioritise delivery.

Requirements
Hands-on AI engineering experience in large, complex corporate or regulated environments.
Robust software engineering background across backend services, APIs, and production systems.
Practical experience operationalising LLM, ML, or AI services beyond prototypes or academic projects.




Hands-on knowledge of RAG, embeddings, vector databases, orchestration, and model evaluation.
Experience with cloud, CI/CD, automated testing, observability, and enterprise release practices.
Understanding of prompt design, evaluation, safety testing, and responsible AI practices.
Relevant tertiary qualifications in technology, engineering, computer science, data, or a related discipline, or equivalent practical experience.
Ability to work effectively with technical and non-technical stakeholders and prioritise work based on impact and risk.

Advantages
Sydney-based hybrid working arrangement.
Supportive and inclusive community with additional support for veterans, Indigenous Australians, and neurodiverse people.
Recruitment-process adjustments are available for people with accessibility requirements.

📌 Ai Engineer Ai Innovation Office Sydney
🏢 Westpac
📍 Sydney