u/One_Tart_8790

We’re not hiring employees. We’re looking for founding builders.

We’re building Vouqis Verify, an AI infrastructure startup focused on helping engineering teams build, verify, and ship reliable AI systems.
We’re assembling our founding team and are looking for:
2 Founding AI Engineers
1 Founding Growth & GTM Lead
This is an equity-only founding role. There is currently no salary.
Everyone joining is taking the same early-stage risk in exchange for meaningful ownership and the opportunity to build something from day one.
If you’re looking for a regular job, this isn’t the right opportunity.
If you’re excited by building an AI startup from scratch, we’d like to talk.

Role 1: Founding AI Engineer (2 Positions)
We’re looking for engineers who enjoy solving difficult problems and shipping products.
Required Skills
Strong coding fundamentals
Python
Git & GitHub
Backend development
Full-stack development (React/Next.js is a plus)
APIs
AI/LLMs
Developer tools
Problem solving
Bonus if you have experience with:
AI Agents
MCP
RAG
Docker
Cloud deployment
DevOps
We’re looking for builders who write code, own problems, and enjoy creating products from zero to one.

Role 2: Founding Growth & GTM Lead (1 Position)
We’re looking for someone who can help us reach our first users and customers.
You’ll work on
GTM strategy
Customer discovery
Finding design partners
Founder-led sales
Community building
Product positioning
Cold outreach
User interviews
Content marketing
Growth experiments
Your goal is simple:
Help us find our first design partners, early adopters, and paying customers.

Who Should Apply
You might be a great fit if you:
Want to build a startup instead of just joining one
Take ownership
Ship consistently
Learn quickly
Are comfortable with uncertainty
Care about long-term value over short-term comfort
Want meaningful equity instead of just a paycheck
Students, recent graduates, and experienced builders are all welcome if you can demonstrate your skills.

What You’ll Get
Founding team equity
Direct influence on product and company decisions
Flexible remote work
Opportunity to grow into a leadership role
Experience building an AI startup from the earliest stage

How to Apply
Send me:
GitHub profile (required for engineers)
LinkedIn
Portfolio or projects you’ve built
Resume (optional)
A short introduction about yourself
Why you want to build a startup instead of taking a traditional job
If you’ve built something you’re proud of, include it. We care more about what you’ve built than what you’ve studied

We’re not hiring employees. We’re looking for founding builders.

I’m building Vouqis Verify, an AI developer tool that helps engineering teams review AI pull requests with confidence before they reach production.

Today, reviewing AI changes often means jumping between prompts, evaluations, code diffs, and multiple tools just to answer one question:

“Is this AI change safe to merge?”

We’re building a workflow that brings that deployment evidence together in one place.

We’re still in customer discovery with an MVP already available (CLI, GitHub Action, and GitHub App), and we’re looking for people who want to help build the company from the ground up.

We’re looking for

2 Founding AI Engineers
You don’t need to know everything, but you should be comfortable with:
Python
Git & GitHub
Backend development
APIs
AI/LLMs
Building products from zero to one
Nice to have:
React / Next.js
RAG
AI Agents
MCP
Docker
Cloud

1 Founding Growth & GTM Partner
You’ll help us:
Find design partners
Conduct customer discovery
Build our GTM strategy
Run outbound outreach
Grow our presence on LinkedIn, Reddit, X, and GitHub
Help us reach our first paying customers

Current Stage
Pre-funding
MVP built
Customer discovery in progress
Looking for our first design partners

What we’re offering
This is an equity-only founding role.
There is no salary today.
If the company raises funding and grows, the goal is to transition founding team members into full-time salaried roles.

This isn’t the right fit if you’re looking for a traditional job.
It is a good fit if you want to help build an AI startup from the earliest stage and are excited by ownership, responsibility, and solving hard engineering problems.

Who should apply?
Engineers who enjoy building, not just coding
People comfortable with uncertainty
Builders who take ownership
Students, recent graduates, or experienced engineers with strong projects to show
People interested in AI infrastructure, developer tools, and startups

Interested?
Send me your profiles at vouqisverify@gmail.com/7995949555
Resume
GitHub (required for engineers)
LinkedIn
Portfolio or projects
A short introduction
Why you want to build a startup instead of joining one
I’d love to connect and see if we’re a good fit.

If you’re excited about taking risks, building from day one, and creating something meaningful with a small founding team, send me a message

When someone changes a prompt,model,or RAG pipeline ,whats your review process before merging?

When someone changes a prompt,model,or RAG pipeline ,whats your review process before merging?

I'm researching reliability challenges around AI agents and would love to hear from people running agents in real-world workflows.

A few questions:

• What failures do you encounter most often?

• How do you currently debug them?

• Roughly how much time do you spend debugging each week?

• Which failures are the most frustrating to diagnose?

Examples:

- Tool failures

- Agent loops

- Context loss

- Memory issues

- MCP server problems

- Authentication failures

- Timeouts

- API failures

- Workflow orchestration issues

I'm particularly interested in understanding what breaks in production and how teams are solving it today.

I'm researching reliability challenges around AI agents, tool calling, and MCP integrations.

For teams building with LangChain, I'd love to learn from your experience:

• What failures do you encounter most often?

• How do you currently debug them?

• Roughly how much time do you spend debugging each week?

• Which issues are the most difficult to identify and resolve?

Examples:

- Tool call failures

- MCP server issues

- Authentication problems

- Timeouts

- Context/state management issues

- Hallucinated tool usage

- Broken integrations

- Agent orchestration failures

Interested in understanding what production teams are seeing and how they're handling it today.

For those using Cursor with MCP servers or external tools:

• What failures do you encounter most often?
• How do you usually debug them?
• Roughly how much time do you spend debugging each week?
• What's the most frustrating issue you've run into?

Examples could be:
- Tool timeouts
- Authentication issues
- Context problems
- Broken tool responses
- Server disconnects
- Performance issues

Interested in learning how teams are handling these problems today.

What failures have you run into?

1. Server disconnects
2. Tool timeouts
3. Auth failures
4. Broken tool responses
5. Context issues

How do you currently debug them?

How much time do you spend debugging per week?

Researching reliability problems in AI agent infrastructure and would love to learn from real-world experiences.

I've been researching MCP infrastructure and I keep coming back to the same question:

Is MCP reliability a real category, or is it just a feature that observability platforms will eventually absorb?

I'm building Vouqis, which currently does:

* MCP auditing

* Runtime gateway/proxy

* Trust scoring

* Reliability testing

The more competitor research I do, the less confident I become about the obvious answer.

Most infrastructure startups fail because they solve a real problem that isn't large enough to become a standalone company.

So I'm trying to challenge my own assumptions.

For teams running MCP servers in production:

1. What failures are you actually seeing?

2. How are you detecting them today?

3. Have you ever blocked an MCP server from production because you didn't trust it?

4. Would you pay for reliability tooling, or would you expect it inside an existing platform?

Looking for honest opinions, especially from teams already deploying MCP-based systems.

Link in comments !

I've been researching MCP infrastructure and I keep coming back to the same question:

Is MCP reliability a real category, or is it just a feature that observability platforms will eventually absorb?

I'm building Vouqis, which currently does:

* MCP auditing

* Runtime gateway/proxy

* Trust scoring

* Reliability testing

The more competitor research I do, the less confident I become about the obvious answer.

Most infrastructure startups fail because they solve a real problem that isn't large enough to become a standalone company.

So I'm trying to challenge my own assumptions.

For teams running MCP servers in production:

1. What failures are you actually seeing?

2. How are you detecting them today?

3. Have you ever blocked an MCP server from production because you didn't trust it?

4. Would you pay for reliability tooling, or would you expect it inside an existing platform?

Looking for honest opinions, especially from teams already deploying MCP-based systems.

Link in comments !