Senior Software Engineer

Rex Software

9d•Remote

About The Position

Rex Software Group is transitioning to an AI-native operating model. This is not about adding AI features to a traditional development process, it is a fundamental shift in how we build and ship software. Our target is Level 4 AI maturity: humans define the architecture, usage scenarios, and acceptance criteria, while AI agents autonomously write, review, and fix code. The AI Native Senior Software Engineer is a senior engineering role for builders who already work this way and can set the standard for others to follow. You will be embedded within a product stream (Rex CRM or Rex PM) as its most experienced AI-native practitioner, expected to deliver production-quality software using AI coding agents as your primary development tool, and expected to shape how the rest of the stream, and over time the wider engineering organisation, works with AI agents. You are not writing every line of code by hand, you are orchestrating AI agents to do it, auditing their output, and ensuring the result meets the architectural and quality standards of the codebase. This is explicitly not a “vibe-coding” role. At senior level, you also carry accountability for the technical decisions and AI-native practices of your stream, not just your own output. This role requires a different profile to a traditional senior software engineer. You must have the engineering maturity to recognise when AI-generated code is correct, performant, and secure, and the judgement to know when it is not, at a level that lets you set standards others follow. You must be comfortable providing constructive, architectural feedback to AI tools the same way a senior engineer coaches a junior developer, with patience, precision, and clear expectations, and you must be equally comfortable coaching human engineers in how to do the same.

Requirements

Several years of demonstrated experience building software using AI coding agents (e.g., Claude Code, Cursor, GitHub Copilot, Cody, Aider, or equivalent) as a primary development tool, with genuine agent-driven development workflows, not just autocomplete
A portfolio, GitHub profile, or personal projects that evidence a mature AI-native development approach, including evidence of setting standards or mentoring others in this way of working
Deep software engineering fundamentals: strong command of system design, data structures, API design, testing strategies, and deployment practices, sufficient to confidently audit AI-generated output and coach others to do the same
Senior-level engineering maturity to distinguish good AI-generated code from bad, including recognising subtle issues with performance, security, edge cases, and architectural fit that AI agents commonly miss
Proven experience working across the full stack (frontend and backend), with the ability to context-switch between layers and take ownership of architecturally significant work
A constructive and patient approach to working with AI tools, and demonstrated ability to coach and mentor other engineers, human or AI-assisted, with clear architectural feedback
Strong communication skills and demonstrated ability to influence technical direction in a collaborative, cross-functional product team

Nice To Haves

Experience with any of Rex's core technologies: PHP/Laravel, Vue.js/React.js, PostgreSQL/MySQL, Docker, Kubernetes, or GCP
Experience in SaaS, proptech, or real estate technology environments
Deep familiarity with prompt engineering techniques, context window management, and strategies for improving AI agent output quality at scale
Contributions to open-source projects or public writing that demonstrate thought leadership in AI-native software development
Experience leading or mentoring within agile product teams with end-to-end delivery ownership
Prior experience helping a team or organisation transition to a new technical practice or operating model

Responsibilities

Deliver production-quality features and fixes within your assigned product stream using AI coding agents as the primary development tool, taking on the most architecturally significant or ambiguous work in the stream
Set the technical direction for how AI agents are orchestrated within your stream, including standards for prompting, context management, and agent-generated code review
Critically audit AI-generated code for correctness, performance, security, and adherence to Rex's coding standards, and coach other engineers to do the same
Own and continuously improve the prompts, context documents, and system instructions (AGENTS.md, Skills, and related documentation) that train AI agents on Rex's architecture, patterns, and conventions for your stream
Identify tasks best suited for AI-assisted development versus manual implementation, and help other engineers build this judgment
Make or strongly influence build-vs-buy and architectural trade-off decisions within your product stream
Own the system architecture within your stream, including database schemas, API contracts, service boundaries, and deployment pipelines, and ensure AI-native workflows respect and reinforce that architecture
Lead technical design discussions, code reviews, and architectural decisions within your product stream, and act as an escalation point for complex technical judgement calls
Set and maintain test coverage standards, ensuring AI-generated code is consistently accompanied by appropriate automated tests across the stream
Debug and resolve the most complex issues across the stack, leveraging both manual investigation and AI-assisted diagnostic techniques
Drive proactive identification and remediation of technical debt, balancing delivery speed with long-term code health at a stream level
Mentor other engineers in AI-native development practices, prompting strategies, and workflow improvements, both within your stream and across the wider engineering organisation
Work closely with product managers, designers, and fellow engineers within your product stream to shape and deliver against roadmap priorities
Represent your stream's AI-native practices and progress to engineering leadership, contributing to group-wide AI development strategy
Document effective patterns and failure modes, and drive adoption of these across teams
Lead by example in sprint ceremonies, technical planning, and cross-stream knowledge sharing
Support the broader engineering team in adopting AI-native development practices through hands-on collaboration, coaching, and structured knowledge transfer