What this role is
We build AI agents that do real work.
Not assistants. Not demos.
Agents that execute workflows end-to-end and produce correct outcomes.
Your job is to:
- build those agents
- and build the systems that make them reliable
This is not prompt engineering.
This is making AI work in production.
What you’ll do
- Build agents that execute multi-step workflows
- Design systems for validation, retry, and failure handling
- Define constraints (schemas, invariants, contracts)
- Add feedback loops (detect → debug → improve)
- Turn failures into reusable systems
What this role is NOT
- Not prompt engineering
- Not one-shot demos
- Not feature-heavy product work
You are building agents that do the work, and the systems that ensure they do it correctly.
Note: This is different from “vibe coding.” You won’t just prompt and accept outputs. You’ll build systems so results are reliable and repeatable.
What we’re looking for
- Strong systems thinking
- Background in:
- infrastructure, backend, or data systems
- developer tools or internal platforms
- Experience building reliable systems (not just features)
- Comfortable debugging complex, ambiguous problems
Important:
LLM experience alone is not enough.
We care about how you make systems reliable.
Good fit if you:
- Think in constraints, invariants, and feedback loops
- Care about correctness, not just output quality
- Have automated real workflows end-to-end
- Prefer building systems over features
Not a fit if you:
- Mostly prompt models and accept outputs
- Have only built demos or prototypes
- Avoid debugging or failure handling
Application (required)
1. Project (GitHub)
An agent system that:
- performs a multi-step task
- includes validation
- handles failures (retry, fallback, etc.)
2. Short answer (5–10 sentences)
Describe a system where an AI agent failed.
What caused it, and how would you fix it?
How we measure success
- Agents complete real workflows with minimal human input
- Outputs are correct by construction
- Failures decrease over time
- New capabilities come from improving the system, not patching outputs
Why this matters
AI models are already powerful.
The bottleneck is making them:
- reliable
- structured
- production-ready
The teams that win will not have better prompts.
They will have systems where agents actually work.
Before you apply
Most engineers won’t enjoy this role.
It requires:
- thinking in systems instead of code
- caring about correctness instead of speed
- debugging behavior instead of writing features
But if this clicks for you,
you’ll be working on the actual frontier of software engineering.
Truewind
