AI-First Principal Engineer (Tech Lead) Full Time NEW

Job description

About

We are building an AI-native SaaS for real estate. The product is a PropTech SaaS platform where LLMs and AI agents are not bolted on top – they are the architecture. Owners, operators and guests interact with the AI system through natural language. AI autonomously generates compliance documents, routes staff, answers guest queries from RAG-indexed knowledge base in real time and orchestrates financial split-payments.

But the product is only half of it.

We are equally an AI-first engineering organization. The way we build is as differentiated as what we build. Our daily development loop – specification, implementation, code review, testing, deployment, on-call response – should run on a custom orchestrator of coding and operations agents. Human engineers operate at the level of intent, architecture, judgment and review. Agents should do most of the typing.

The role

This is the role that defines how that system works and how we build the product.

This is a hands-on technical leadership role at the core of the company’s future, with a single defining characteristic: you will architect both the product development & testing approach and the AI based engineering system that builds it.

You will own the design, construction and operation of an internal AI-first development platform – the orchestrator, the agent fleet, the specification format, the evaluation harness, the CI/CD wiring – that lets a small team of senior humans ship at the velocity and surface area of a larger conventional team. You will decide what the human engineering team does, what agents do.

You will simultaneously own the production AI layer of the product itself – RAG pipelines, agent workflows, prompt strategy, vector memory – because the same intuition that makes the engineering org work makes the product work.

You will write production code daily. You will read agent diffs daily. You will build and tune evals continuously. 

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which engineering tasks AI does well today, which it does not and how to close that gap inside our codebase deliberately.

What you will own & do

AI-First Engineering System (the differentiator)

• Build and operate the AI-first engineering system: design the orchestrator (task decomposition, agent coordination, review gates, deployment/rollback) and manage a fleet of coding, review, testing, and ops agents.
• Establish a spec-driven development model: convert product intent into machine-readable specs that agents can implement, test, and evolve reliably over time.
• Define governance and trust: set clear boundaries for agent autonomy vs. human approval, implement verification layers (evals, tests, canaries, traces), and codify standards as machine-readable rules.
• Continuously optimize performance and autonomy: run evaluation frameworks (A/B tests, drift detection, cost/latency tracking), make model/infra decisions, and increase the share of SDLC handled autonomously each quarter.

Technical Architecture & Strategy

• Define and own the full technical architecture of the SaaS platform: service boundaries, API design, data models and infrastructure
• Make fast, reasoned architectural trade-offs under startup timelines without accumulating critical technical debt
• Set the technical direction for AI integration in the product: the AI layer is a core product pillar not a feature.

Hands-On Engineering

• Write production-grade code – much of it through and alongside agents, all of it to your standard (stack to be defined).
• Build and maintain frontend interfaces where needed (stack to be defined). Own database design and query performance
• Design and ship real-time systems: live dashboards, event-driven pipelines.

Product AI & LLM Layer

• Own the end-to-end AI layer of the product (from prototype to production): lead adoption of new LLM tools, build and operate RAG pipelines, manage vector databases, and ensure only production-ready solutions are used.
• Design and orchestrate AI workflows and prompt strategies to power core use cases (guest chat, operator briefings, compliance docs, natural-language commands) while continuously evaluating and integrating cutting-edge AI capabilities.

Integration Layer

• At a certain point own the full integration architecture across all third-party systems – IoT platforms, smart locks, payment rails, government and regulatory APIs.
• Ensure the integration layer is reliable, observable and extensible – future integrations include additional regulatory APIs and secondary trading infrastructure.

Engineering Leadership

• Lead a small, high-caliber hybrid team (engineers + AI agents): set technical standards, run code reviews, unblock delivery, and ensure consistent execution under pressure while maintaining high quality.
• Act as the bridge between business and engineering: translate product needs into clear, agent-executable work, build an AI-first engineering culture, and mentor the team on effectively working with AI (what to delegate vs. retain).

Infrastructure & Reliability

• Own and build scalable infrastructure from scratch: set up CI/CD with agent participation, design cost-efficient cloud architecture and ensure production readiness for live operations.
• Implement full observability and reliability practices: monitoring, alerting, agent traceability, cost/latency tracking, API health and an on-call capable system that is transparent and controllable.

Skills

What we are looking for

• 5-7+ years in software engineering, with technical leadership experience of a team under direct supervision.
• Proven 0 → 1 experience – you have built engineering teams, systems and architecture from scratch, not joined an already-scaled org.
• Strong production experience with modern backend ecosystems (TypeScript / Node.js or equivalent) – you can design and evolve service architectures at speed.
• Solid frontend engineering competence (React, Vue.js or equivalent) – enough to architect, review and unblock teams.
• Deep understanding of data layer design (relational databases such as PostgreSQL or equivalent), including multi-tenant architectures, performance and access control models.
• Proven ability to select, evolve and own the technology stack based on product needs, not trends.
• Comfortable working across the full stack when needed — but with clear architectural judgment on trade-offs
• Daily, deep, native use of agentic coding tools on real production codebases – not toy demos. You have shipped meaningful features where the majority of code was agent-authored under your direction.
• Hands-on experience designing or significantly extending an internal AI development workflow – orchestration of multiple agents, spec-to-PR pipelines, automated review, agent-driven test generation, or comparable systems.
• Production experience with LLM/AI integration in user-facing product – RAG pipelines, vector databases, prompt engineering, agent workflows. Features real users rely on, not demo projects.
• Rigorous instinct for evaluation – you treat LLM outputs as something to be measured, not trusted, and you have built evals to prove it.
• Demonstrable track record of integrating multiple complex third-party APIs into a coherent, stable product.
• Experience building real-time systems
• Comfortable setting up CI/CD and cloud infrastructure from zero.
• Advanced English – oral and written.

Strong advantage (Nice to have)

• You have built or open-sourced an agentic dev tool, orchestrator, eval harness or comparable internal-facing system.
• Familiarity with orchestration frameworks and agent observability tooling
• Experience in PropTech, Fintech, hospitality technology or multi-sided marketplace platforms.
• Background with IoT protocols and smart home device ecosystems.
• Experience integrating government or regulatory APIs.
• Familiarity with tokenization infrastructure or regulated fintech architecture.

Why this role

You will simultaneously build the first AI-native operating system for real estate in Dubai and the AI-first engineering org that ships it. Both are differentiators, both are hard and both are yours.

• Genuine architectural ownership – of the product and of the way we build it. This is not a “maintain and improve” role.
• Clear growth trajectory – this role evolves into Head of Engineering and eventually CTO. We are building that succession deliberately.
• Direct access to founders – short decision loops, no middle management, real influence on product direction.
• Technically ambitious surface area – AI agents, RAG, IoT, real-time systems, government API integrations and eventually (later) tokenization infrastructure. All in one product, all built by a system you design.
• Bonuses – we know how to appreciate the hard work. Bonuses will be assigned based on performance at CEO’s discretion.
• Equity participation – at a defined point you will own a share of what you build.
• Great office in the heart of Dubai’s business life – Once Central Plaza