Founding Solutions Architect

Unhurdled; Dubai; On-site; AED 400,000 - 680,000; Full time

Job summary

Unhurdled in Dubai seeks its founding Solutions Architect to design the path from ambition to production. You’ll work with customers and engineers to define the foundations early, proving uncertain parts and laying out a clear route from first deployment to a team-wide dependable solution.

You’ll assess customers’ systems, data flows, access controls and constraints, design architectures for AI workflows with clear ownership, and translate security requirements into practical decisions.

Qualifications

• Understand the customer’s systems, data flows, access controls and constraints before proposing a solution.
• Design architectures for AI workflows, integrations and human approvals, with clear ownership at each boundary.
• Build technical proofs where needed to test assumptions about data quality, access, latency or reliability.
• Translate security and infrastructure requirements into practical decisions with customer technical teams.
• Work through implementation with engineers and turn successful patterns into reusable architecture and documentation.

Responsibilities

• You have carried a design into production.
• You know AI systems beyond the diagram.
• You prove the uncertain parts yourself.
• You can make architecture fit an early company.
• You make trade-offs clear enough to act on.

Job description

Design the path from ambition to production.

A useful AI workflow has to live somewhere: between an inbox and a system of record, across an approval boundary, inside a team’s existing responsibilities. Getting those connections right is what makes the whole thing possible.

As our founding Solutions Architect, you’ll design that foundation. You’ll work with customers and engineers to make the hard decisions early, prove the uncertain parts and leave a clear path from a first deployment to something a team can depend on.

What you’ll build with us

• Understand the customer’s systems, data flows, access controls and constraints before proposing a solution.
• Design architectures for AI workflows, integrations and human approvals, with clear ownership at each boundary.
• Build technical proofs where needed to test assumptions about data quality, access, latency or reliability.
• Translate security and infrastructure requirements into practical decisions with customer technical teams.
• Work through implementation with engineers and turn successful patterns into reusable architecture and documentation.

Who we’re looking for

You have carried a design into production.

You have integrated software with an existing business environment and stayed close enough to learn where the design failed. You can explain the permissions, data models, APIs and infrastructure choices, along with their operational consequences.

You know AI systems beyond the diagram.

You have built or deployed an AI workflow used by real people. You understand tool permissions, state and memory, evaluations and failure modes. You can say where an agent is useful, where a simpler system is sufficient and where a person must decide.

You prove the uncertain parts yourself.

You can enter the codebase, inspect a payload or build a technical proof. Tools such as Codex or Claude Code are part of that work, and you review what they produce. A design is not settled because the generated explanation sounds plausible.

You can make architecture fit an early company.

You have delivered with limited time, people or infrastructure. You can separate a constraint that must be solved now from an abstraction that can wait, and choose what to build, buy or ask a specialist to deliver.

You make trade-offs clear enough to act on.

You can work through access and security requirements with a customer, write a brief an engineer can implement and explain the cost of a decision to an operator. You surface uncomfortable constraints before they become promises in a proposal.

How we work

You choose useful work without waiting for a task queue. When a route closes, you investigate another, bring a recommendation and follow through. You ask for help before a blocked decision becomes a missed commitment.

You catch weak assumptions, generic reasoning and fabricated details. You check AI outputs before passing them to a colleague or customer. The tools can do more of the work; you still own its quality.

You tell the truth early.

You flag doubts while there is still time to act. You can disagree with a founder, admit uncertainty and change your mind when the evidence changes. Manufactured agreement helps nobody.

You take endurance seriously.

You want to do demanding work well over time. You protect the habits and recovery that sustain your judgement, make realistic commitments and raise unsustainable workloads before exhaustion makes the decision for you.

Your first chapter

Map a customer environment, identify the critical constraints and prove the highest-risk connection. Turn that work into an architecture that the deployment team can confidently implement.

Why join at the beginning?

You’ll help decide what we build, which customers we pursue and what good work looks like here. Expect incomplete systems, direct customer contact and decisions whose consequences you can see. We want people who enjoy making something work while the answers are still taking shape.

Early-stage experience carries more weight here than a corporate title. What matters is evidence that you can build, learn and take responsibility when resources are lean. If you developed that judgement inside a larger organisation or took an unusual route, show us the work.

YOUR NEXT CHAPTER

Let’s build
something useful.

Walk us through a system you helped put into production, a constraint you tested yourself and a trade-off you would make differently today. Be clear about what you personally owned.