Engineering Manager

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.

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. Dev Ops 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, Dev Ops, 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.

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.

• Own the team’s delivery from requirements through production, with clear scope, acceptance criteria, priorities, and accountability.
• Coach engineers to use AI agents effectively for implementation, debugging, refactoring, testing, and documentation; contribute directly when needed.
• Help the team break complex work into reviewable tasks, provide relevant context to agents, and validate results against product requirements.
• Review designs and critical code changes, challenge unsupported AI assumptions, and ensure changes remain understandable and maintainable.
• Embed automated tests, peer review, CI checks, security checks, and end-to-end validation into delivery; keep human review accountable for release decisions.
• Partner with Product, QA, and Dev Ops to resolve blockers, prepare releases, and address production issues and recurring defects.
• Run regular one-to-ones, give actionable feedback, support career growth, and manage workloads sustainably.
• Improve team practices using evidence from delivery lead time, escaped defects, reliability, and AI usage costs.

• Typically 7+ years in software engineering and 2+ years leading engineers or managing a team; equivalent demonstrated impact is welcome.
• Strong hands-on experience building production applications, with sound knowledge of APIs, databases, testing, CI/CD, and cloud systems.
• Practical experience using AI coding assistants or agents to deliver production software and helping other engineers adopt them effectively.
• Ability to review and debug AI-generated code, identify missing edge cases, and select appropriate automated and human validation.
• Strong planning, communication, and coaching skills, with a record of creating clarity and delivering through a team.

Experience building reusable agent workflows, integrating engineering tools through APIs or MCP, or delivering software in fintech or another regulated environment.

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.