Confidential
Head of AI Engineering & Architecture
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About the Role
We are building the next generation of AI-native platforms for real estate — spanning intelligent marketplaces, agentic transaction workflows and data-driven investment tooling — with engineering in London and operating businesses in Saudi Arabia and the wider Gulf. The Head of AI Engineering & Architecture owns the end-to-end technical architecture of the platform and leads the engineering organization that builds it. The role converts product and commercial ambition into a scalable, secure and compliant technology estate, and sets the engineering standards, tooling and culture for a fast-scaling AI venture.
Key Responsibilities
Architecture
- Define and own the target-state architecture across the platform: multi-tenant SaaS core, LLM and agentic orchestration layers, retrieval and vector infrastructure, data platform and APIs.
Engineering leadership
- Build, lead and scale the AI engineering function in London — hiring, organization design, performance management and succession — while coordinating with distributed development teams in the Gulf and India.
AI/ML platform
- Establish the model strategy: selection and evaluation of foundation models, fine-tuning and RAG patterns, agent frameworks, guardrails, evaluation harnesses and cost/latency optimization.
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MLOps & reliability
- Stand up production-grade MLOps and LLMOps — CI/CD, observability, model monitoring, drift detection, prompt and version management — with clear SLOs for reliability and performance.
Cloud & infrastructure
- Own cloud architecture and infrastructure economics (AWS/Azure/GCP), including data residency design for Saudi-hosted workloads and multi-region deployment.
Security & compliance
- Embed security and privacy by design; ensure the platform meets UK GDPR and Saudi PDPL requirements and supports sector regulatory obligations (REGA/FAL, and SAMA where financial-services use cases apply), working with legal and compliance counterparts.
AI governance
- Institute AI governance: model risk management, human-in-the-loop controls, auditability, red-teaming and responsible-AI standards suitable for board and investor scrutiny.
Stakeholder partnership
- Partner with the Head of AI Product on roadmap feasibility, technical discovery and build/buy/partner decisions; present architecture and delivery status to the CTAIO, board and investors.
Vendors & partnerships
- Manage vendor and partner ecosystem — model providers, cloud, data and tooling — including commercial negotiation and technical due diligence.
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Candidate Profile
- 10+ years in software engineering with 5+ years leading engineering or architecture teams, ideally including a scale-up or venture-built environment.
- Demonstrable delivery of production LLM/GenAI systems at scale — agentic workflows, RAG pipelines, evaluation and safety tooling — not solely proofs of concept.
- Deep architectural command of distributed systems, event-driven design, APIs, data platforms (streaming and warehouse/lakehouse) and modern DevSecOps.
- Hands-on credibility in Python and at least one of TypeScript/Go/Java; strong grasp of vector databases, orchestration frameworks and inference optimization.
- Track record of hiring and developing high-performing engineering teams in London or a comparable talent market.
- Working knowledge of data-protection regimes (UK GDPR essential; Saudi PDPL desirable) and experience operating in regulated or multi-jurisdiction environments.
- Sector exposure to PropTech, marketplaces or fintech is an advantage.
- Executive-level communication — able to translate deep technical trade-offs for boards, investors and non-technical leadership.
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