Senior Engineering Manager

Soar; Greater London; On-site; GBP 120,000 - 170,000; Full time

Job summary

Soar in London is seeking a Senior Engineering Manager to lead multiple AI-enabled engineering teams and drive the development of our AI-native home-financing platform. You will stay technically hands-on while mentoring managers, shaping roadmaps, and ensuring delivery quality across backend, frontend, and infrastructure.

You will establish reusable agent workflows, define validation controls for AI-generated changes, and partner with Product, Security, and DevOps to ensure reliable, scalable

Qualifications

• 10+ years in software engineering and 4+ years in engineering leadership or equivalent impact.
• Strong judgment across architecture, APIs, databases, cloud infra, testing, and production operations.
• Experience embedding AI coding tools and agents into team workflows with measurable improvements.
• Ability to design review and validation controls for AI-generated changes including data protection.
• Track record of developing leaders, clear communication, and delivering complex initiatives.

Responsibilities

• Own delivery, technical quality, and operational readiness across multiple teams; translate priorities into plans.
• Lead and coach engineering managers and senior engineers; foster clarity, feedback, collaboration, and sustainable performance.
• Define how teams use AI agents throughout the lifecycle: planning, implementation, code review, testing, and documentation.
• Establish reusable agent workflows, shared context, and automated quality gates to improve delivery.
• Guide architecture and cross-team decisions; challenge solutions on correctness, security, scalability, maintainability, and cost.
• Partner with Product, QA, Security, and DevOps to resolve dependencies and validate releases via real user journeys.
• Own hiring, capability, and engineering performance; measure AI impact through delivery quality, lead time, reliability, and cost.

Skills

Leadership

AI tooling

Architecture judgment

Career development

Cross-functional collaboration

Job description

About 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.

About The Role

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 across software design, development, testing, and delivery. We are looking for a Senior Engineering Manager who can lead multiple teams, develop engineering leaders, and turn AI-assisted development into a reliable, scalable engineering capability. You will remain technically engaged and accountable for the quality and business impact of what your teams ship.

Key Responsibilities

• Own delivery, technical quality, and operational readiness across multiple engineering teams; translate business priorities into clear plans and measurable outcomes.
• Lead and coach engineering managers and senior engineers. Build a culture of clarity, constructive feedback, collaboration, and sustainable performance.
• Define how teams use AI agents throughout the development lifecycle, including planning, implementation, code review, testing, and documentation.
• Establish reusable agent workflows, shared context, and automated quality gates that improve delivery across teams.
• Guide architecture and cross-team decisions; challenge generated solutions on correctness, security, scalability, maintainability, and cost.
• Partner with Product, QA, Security, and DevOps to resolve dependencies and ensure releases are validated through real user journeys.
• Own hiring, organizational capability, and engineering performance. Measure AI impact through delivery quality, lead time, reliability, and cost.

Requirements

• Typically 10+ years in software engineering and 4+ years in engineering leadership, including responsibility for multiple teams or developing engineering managers; equivalent demonstrated impact is welcome.
• Strong technical judgment across application architecture, APIs, databases, cloud infrastructure, automated testing, and production operations.
• Demonstrated experience embedding AI coding tools and agents into team workflows, with concrete examples of measurable improvements.
• Ability to design review and validation controls for AI-generated changes, including human accountability, data protection, and appropriate agent permissions.
• A record of developing leaders, communicating priorities clearly, listening to teams, and delivering complex initiatives across functions.

Nice to have :

Experience in fintech or other regulated environments; agent orchestration, evaluation frameworks, shared context systems, or internal developer platforms.

What success looks like :

Multiple teams deliver predictably with fewer escaped defects; managers and engineers grow in capability; AI workflows demonstrate measurable gains without weakening reliability, security, or ownership.