[YOUR NAME]
[Target role, e.g. AI product engineer] | [City / region]
[Professional email] | [GitHub / portfolio] | [Optional Dopple link]

PROFILE
[The users or problems you build AI systems for, the scope you own,
and a concrete strength supported by the work below.]

EXPERIENCE
[Role] — [Organization] | [Month year–Month year]
- Built [agent or AI workflow] for [user problem], owning [scope]
  from [requirements] through [prototype / production release].
- Designed the harness around [tools, permissions, state, retries,
  or stopping conditions]. Resolved [specific failure or tradeoff].
- Created evals for [real tasks and failure cases], using [grader
  and human review]. Caught [regression] before [release decision].
- Improved [quality, reliability, latency, or cost] through [change].
  Evidence: [measurement and baseline, or clearly observed result].

[Earlier role] — [Organization] | [Month year–Month year]
- [Relevant ownership, debugging, delivery, or collaboration.]

SELECTED AI SYSTEM
[Project] | [Date] | [Public demo, code, or case study if available]
- Agents: [Why one or several; task boundaries, handoffs, shared
  state, and how you handled an incomplete or failed run.]
- Context: [How you selected, retrieved, or compressed information;
  how you checked relevance, freshness, and source attribution.]
- Memory: [What persisted, its user or project scope, and how it
  was corrected, expired, or deleted. Explain why it was needed.]
- Validation: [Eval coverage, a failure you found, and the resulting
  change. State deployment status and your personal contribution.]

ENGINEERING CAPABILITIES
[Keep only capabilities demonstrated above: agent harnesses;
eval design and graders; agent orchestration; context and retrieval;
memory design; tracing and debugging; cost and latency analysis.]
[Name models, frameworks, or infrastructure only where they explain
a decision or match a relevant requirement you can substantiate.]

EDUCATION AND TRAINING
[Qualification or relevant training] — [Institution] | [Date]