Product Engineer AI Agents, IES LATech & GIS

Amazon is hiring a Product Engineer to focus on Agentic AI in Australia building the capabilities that let AI agents act reliably on real operator workflows. Youll join a specialist team of Product Engineers Scientists and Developers within Amazons emerging-markets technology organisation focused on sustainable growth across Latin America and Australia.

Your customers are operators. Planners ops managers analysts support leads. They make hundreds of small judgement calls a day and your job is to give them agent capabilities that absorb the repetitive ones so they can focus on the calls that actually matter.

In this role you are part builder part product owner. You scope the pain design the mechanism build the prototype instrument the metrics then iterate in production. You build the primitives integrations and mechanisms behind agentic systems integrating large language models with internal data and trading off latency cost and accuracy with judgement not by recipe.

Key job responsibilities
- Review agent runs in production triage operator corrections and determine which are bugs versus signal that the autonomy boundary needs to move.
- Pair with operators on workflows you are scoping. Watch them work ask why behind every step and translate implicit rules into mechanisms the agent can execute.
- Close the feedback gap by ensuring every operator correction becomes a test an eval or a prompt update not lost noise.
- Own the roadmap end to end. Prioritise demo weekly and articulate design choices in writing.
- Partner with operations leaders in each market to identify which workflows are agent-ready and which still need a human in the loop.
- Translate ambiguous operator pain points into concrete measurable mechanisms with clear success criteria.
- Monitor trust score accuracy latency and cost per decision. Dig into regressions before they compound.

- Experience developing operational processes and technologies
- Working knowledge of LLM-based applications: prompting tool/function calling evaluations and the trade-offs between latency cost and accuracy.
- Experience integrating with internal or external APIs data sources and services.

- Experience building AI agents or agentic systems in production.
- Familiarity with composable agent primitives: tool use memory retrieval evaluation harnesses and model-swap patterns.
- Track record of working backwards from operator or customer workflows.
- Experience defining metrics for trust accuracy or autonomy and using them to gate rollout.
- Experience operating in ambiguity; taking a vague business problem and converging it into a shipped mechanism.

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