주요업무
- Build and ship LLM-powered applications and AI agent workflows
- Design systems for reasoning, planning, memory, tool uuse and multi-step execution
- Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions
- Integrate LLMs with APIs, databases, search, internal services, and external tools.
- Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behaviour
- Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions
- Debug AI systems across the entire stack—from model behaviour and prompts to orchestration, backend services, and product UX
- Optimise AI systems for quality, latency, and cost
- Work closely with product and engineering teams to turn ambiguous product problems into working AI solutions
- Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement
[Outcome]
- AI features reach production quickly and deliver measurable user impact
- LLM-powered workflows are reliable, scalable, observable, and maintainable
- AI quality improves through systematic evaluation, experimentation, and iteration
- AI workflows become increasingly predictable, efficient, and cost-effective
- Complex AI capabilities are translated into simple, intuitive user experiences