Contribute to the full AI product lifecycle: discovery, requirements definition, development, testing, and deployment
Design, build, and iterate on LLM-powered agentic workflows for complex, data-intensive use cases, applying sound orchestration patterns and tool-use design
Translate business and user needs into clear, actionable product requirements and agent configurations
Define and monitor product performance metrics and acceptance criteria for AI outputs in production — covering accuracy, latency, cost, and auditability
Manage the post-launch product lifecycle: track performance, gather user feedback, and contribute to model or feature refresh cycles
Contribute to system optimisation across performance, cost, and operational constraints
Collaborate with governance teams to ensure AI outputs meet internal quality, compliance, and interoperability standards ...