Founding Harness Engineer – AI Agents

Bolder Apps

. Build the agent runtime: context, tool choice, approval waits, and knowing when to act, ask, or stay quiet .

Posted 10/13/2026contractRemote • Argentina, Colombia, Mexico, Uzbekistan, GeorgiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and tuning LLM agents for production, with a focus on safe tool execution, prompt management, and backend development in Python or TypeScript. Strong communication skills facilitate effective collaboration with founders and users to translate feedback into actionable improvements.

Highest-signal resume keywords

LLM Agent DevelopmentBackend Development in PythonEval and Regression SuitesSafe Tool ExecutionExcellent Communication Skills

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonTypeScriptEval SuitesRegression TestingPrompt ManagementAsync JobsState ManagementTool-Using LLM AgentsTradeoff AnalysisMulti-Tenant Data Risk

Soft Skills

Excellent Written CommunicationExcellent Verbal CommunicationGood Instincts for Agent Initiative

Tools & Technologies

SlackGmailNotionLinearBraintrustLangfuseLangSmith

Certifications & Qualifications

MCP

Industry Keywords

Agent RuntimeProactive BehaviorApproval GatesCustomer Data SeparationFeedback Implementation

Tech Stack

Tools & technologies

PythonTypeScript

About the role

Key responsibilities & impact

Build the agent runtime: context, tool choice, approval waits, and knowing when to act, ask, or stay quiet
Write and keep tuning the system prompts; ensure proactive behavior without spamming the team channel
Own the eval and regression harness; test prompt changes like code
Make long-running work survive interruptions, retries, and flaky APIs
Implement guardrails, including approval gates for risky actions and strict customer data separation
Work directly with the founder and early users, turning feedback into shipped behavior

Requirements

What you’ll need

You've shipped tool-using LLM agents to real users in production. Demos and prompt wrappers don't count
Good instincts for agent initiative, and the test results to back them up
Hands-on experience building eval and regression suites for LLM behavior
You understand safe tool execution, prompt injection, and multi-tenant data risk
Solid backend skills in Python or TypeScript (async jobs, retries, state)
You take a fuzzy problem from prototype to production and can explain the tradeoffs along the way
Excellent written and verbal communication; you can explain a tradeoff to a founder or a user without a whiteboard
Nice to have: MCP, or integrations with tools like Slack, Gmail, Notion, or Linear
Nice to have: Eval and tracing tools such as Braintrust, Langfuse, or LangSmith
Nice to have: You've been an early engineer at a startup before and still liked it

Benefits

Comp & perks

Founding equity
A direct line to the founder and early users, with no layers in between
Remote from Latin America or Eastern Europe, with solid overlap with US Pacific time
The tools and models you need to do the job properly
A small, experienced team that trusts you to run your own work