About The RoleAbout Soar and how we build :Soar is building an AI-native home-financing platform for Saudi Arabia, and the AI-driven engineering system that builds it. Our product spans origination, credit decisioning, loan management, collections and investor products, all under SAMA regulation.
We build differently. Our engineers don't hand-write code; they specify, direct and review AI agents that do. If that sounds like where engineering is going, you'll fit here.
How we work :
Agents write the code. Engineers own the spec, the context, the tests and the review. DevOps doesn't hand-manage servers either.
Context engineering is the core skill. Agents work from a shared knowledge graph of business rules, architecture principles and money-movement rules that we keep current.
We build our own agents. Requirements, DevOps, PM and self-testing agents run on our MCP/A2A framework to automate the SDLC end to end.
Full stack by default. AI closes skill gaps, so engineers work across backend, frontend and infrastructure, with cross-domain reviewers.
Regulated means rigorous. Money-movement logic gets human review, every time. KSA data residency shapes which models and tools we use and where they run.
What every role here needs: you already use AI coding agents (Claude Code, Cursor, Codex or similar) as your main way of working, not as autocomplete. You can show us real workflows you've built, where they broke, and what you changed.
Role Summary:We rely heavily on AI to build, test, and improve software. We are looking for a hands-on Engineering Manager who can lead a team of engineers working with AI agents, keep delivery focused, and ensure every release meets a high quality bar. You will combine people leadership with practical technical oversight and help your team become more effective every week.Key Responsibilities:
RequirementsRequirements:
Nice to have :Experience building reusable agent workflows, integrating engineering tools through APIs or MCP, or delivering software in fintech or another regulated environment.
What success looks like :The team ships tested features predictably, resolves issues quickly, and demonstrates improving quality and productivity. Engineers use AI effectively while understanding and owning the systems they deliver.
