Landmark Digital
As a part of the Landmark Group, a renowned retail and hospitality conglomerate in the Middle East, North Africa, and India, Landmark Digital is the dynamic digital arm of Landmark Retail, serving as the cornerstone of our omnichannel business strategy.
Headquartered in Dubai, UAE, we oversee the digital operations of eight leading brands across diverse geographies, with ambitious plans for expansion into new territories and functions. Joining us means becoming a vital part of the Middle East's most significant bricks-to-clicks success story, boasting an impressive year-on-year growth rate exceeding 100%.
Comprising a talented workforce of over 700 professionals across diverse domains, Landmark Digital spearheads various functions including Enterprise and E-commerce Tech, Product Management, User Design, and MarTech, among others. With our futuristic outlook, we are committed to delivering seamless digital experiences to our customers.
Job Specification -
We are looking for a Lead AI Architect to lead the design and architecture of AI products, from problem definition and technical discovery through production delivery and continuous improvement. You will turn business and product goals into secure, scalable, measurable AI solutions and help teams choose the right approach across a fast-changing AI landscape.
You will work closely with product owners, enterprise architects, engineering, data, security and operations teams. This is a hands-on technical leadership role: you will own solution architecture, validate critical design choices through prototypes and reference implementations, and guide teams through delivery.
Key Focus Areas:
- Partner with product owners to define AI use cases, user journeys, feasibility, business outcomes and acceptance criteria. Challenge when conventional software or analytics is a better fit than AI.
- Lead end-to-end AI product architecture across experience, application, model, data, integration and infrastructure layers. Document decisions, trade-offs, dependencies and non-functional requirements.
- Design appropriate solutions using predictive ML, generative AI, retrieval-augmented generation (RAG), multimodal models and agentic workflows. Use autonomous or multi-agent designs only where they add value.
- Work with enterprise architects to align solutions with target architectures, integration patterns, platform standards and governance. Build reusable reference architectures and components without duplicating enterprise capabilities.
- Evaluate models, platforms, frameworks and vendors through structured experiments. Recommend build-versus-buy decisions based on quality, security, latency, total cost, portability and operating needs.
- Guide engineering teams through implementation, architecture and code reviews, integration and production readiness. Prototype high-risk assumptions and mentor engineers and other architects.
- Embed responsible AI and security by design: privacy, access controls, permission-aware retrieval, tenant isolation, prompt-injection defenses, safe tool use, audit trails and human approval for high-impact actions.
- Define evaluation, testing, monitoring and lifecycle controls for models, prompts, retrieval and agents. Plan fallbacks, failure handling, rollbacks and incident ownership with platform and operations teams.
- Track the AI landscape and translate developments into practical roadmaps and guidance. Communicate decisions clearly to technical teams, product owners and senior stakeholders.
Additional experience
- Regulated or enterprise environments; AI platform/CoE design; Model Context Protocol (MCP) and other tool-integration patterns; knowledge graphs; model-serving and open-weight deployment; domain-specific AI validation; architecture or cloud certifications; mentoring across multiple product teams. Specific frameworks and certifications are advantages, not substitutes for delivery evidence.
Knowledge, Skills & Experience Relevant Job Experience
- A strong track record in software, solution or AI architecture, with evidence of shipping and operating enterprise-grade products. Indicative experience: 8+ years in engineering/architecture, including 3+ years working on AI/ML solutions; equivalent demonstrated experience is welcome.
- Direct experience taking an LLM-based or agentic product into production, beyond demos and proofs of concept. Ability to explain design choices, evaluation results, operating costs and lessons from real failures.
- Broad understanding of the AI landscape: foundation and open-weight models, conventional ML, RAG, embeddings, retrieval/reranking, context engineering, fine-tuning, multimodal systems and agent orchestration. Sound judgment about when each is appropriate.
- Hands-on ability to prototype and review production code, preferably in Python and at least one product/backend stack. Strong API, distributed-system, data-pipeline and enterprise integration design skills.
- Experience with at least one major cloud and its AI services, plus containerized or managed deployment, CI/CD, infrastructure automation, MLOps/LLMOps and observability.
- Practical experience with AI evaluation, grounding quality, safety testing, model/prompt versioning and cost/latency optimization. Familiarity with identity, least privilege, data boundaries and secure tool/API access.
- Ability to influence without relying on reporting authority, resolve architectural trade-offs and work effectively with enterprise architects, engineering teams and product owners.
- Clear written and verbal communication: architecture diagrams, decision records, delivery guidance and explanations suitable for business audiences.
- Degree in computer science, engineering or a related field, or equivalent practical experience.
What Success Looks Like
- Product goals become clear, agreed architectures and measurable delivery/evaluation criteria.
- AI products meet agreed quality, safety, reliability, latency and cost targets in production.
- Teams reuse approved patterns, and architecture decisions remain aligned with enterprise standards.
- Product owners and engineers can make faster, better-informed trade-offs as the AI landscape changes.
