AI Lead Dubai

AI Lead to take ownership of AI product development, multi-model LLM systems, agentic AI solutions, data acquisition pipelines, and enterprise AI technology.

The successful candidate will combine strong software engineering, artificial intelligence, product management, and enterprise technology experience. This is a hands-on leadership position for an AI professional who can build production-grade AI systems, manage AI infrastructure and costs, establish compliance standards, and develop AI products for enterprise clients.

The AI Lead will initially operate as a hands-on technical and product leader, with responsibility for building the platform and subsequently recruiting and developing an AI engineering and product team as the workload expands.

Job Responsibilities

AI Product Strategy and Roadmap

• Own and manage the AI product roadmap across the platform.
• Prioritize product development based on actual user requirements, usage data, business value, and customer needs.
• Identify opportunities to develop new AI-powered products and features.
• Translate business, operational, and client requirements into practical AI products and technology solutions.
• Balance internal business requirements with the development of scalable, market-ready AI solutions.
• Manage multiple AI product initiatives running on shared technology infrastructure.
• Evaluate competing priorities and determine which AI features and products should be developed, improved, or discontinued.
• Take AI products from initial concept and development through production deployment and enterprise adoption.

AI Engineering and LLM Systems

• Design, build, and maintain production-grade AI and Large Language Model (LLM) systems.
• Develop and manage multi-model AI architectures using multiple LLM providers.
• Build model-routing systems that select appropriate AI models based on performance, availability, cost, and use case.
• Develop fallback mechanisms to maintain service availability when an AI provider experiences an outage or model-related issue.
• Monitor model behavior, output consistency, latency, reliability, and inference costs.
• Troubleshoot model changes, schema inconsistencies, provider outages, unexpected outputs, and other production issues.
• Maintain API integrations with leading LLM and AI service providers.
• Evaluate new AI models and providers and determine when an existing provider should be replaced or supplemented.
• Build and maintain agentic AI systems and tool-calling workflows.
• Develop and troubleshoot MCP or similar agentic technology frameworks.
• Debug failed agent steps, tool calls, API integrations, and AI workflow processes.
• Ensure AI systems are reliable, scalable, secure, and suitable for enterprise deployment.

Data Acquisition and Web Scraping

• Design, develop, and maintain automated data acquisition and web scraping pipelines.
• Source structured and unstructured information from the open web and other approved data sources.
• Build end-to-end data pipelines covering data collection, extraction, cleaning, processing, storage, and integration with AI products.
• Maintain scraping systems as websites change their structure, APIs, access policies, or technical configurations.
• Handle rate limits, headers, proxy infrastructure, selector changes, and other technical challenges associated with maintaining large-scale data pipelines.
• Monitor data quality and ensure acquired information remains accurate and usable by AI applications.
• Integrate third-party APIs and approved data sources into the AI platform.
• Evaluate the legal, regulatory, privacy, and compliance implications of data acquisition activities.

AI Compliance, Governance and Data Protection

• Establish AI compliance requirements from the beginning of product development.
• Build appropriate audit trails and logging into AI applications.
• Implement human-in-the-loop approval mechanisms where required.
• Ensure appropriate data residency and enterprise data protection requirements are considered during system design.
• Establish controls for the responsible use of AI-generated and AI-retrieved information.
• Ensure source-cited or grounded AI responses can be verified before being delivered to enterprise clients.
• Pay particular attention to accuracy, traceability, and data governance when AI solutions are deployed in sensitive sectors such as banking and healthcare.
• Understand relevant privacy and data protection requirements applicable to AI systems operating in the GCC.
• Assess compliance considerations relating to personal data, web data acquisition, copyright, terms of service, and applicable regulations.
• Identify and escalate legal, privacy, security, or compliance risks before they become production issues.

API, Infrastructure and AI Cost Management

• Manage API credentials and access across multiple AI, LLM, data, and technology providers.
• Establish processes for API key provisioning, rotation, monitoring, and secure management.
• Track AI inference and API consumption costs.
• Manage and optimize AI infrastructure and model usage to achieve an appropriate balance between cost, performance, and quality.
• Monitor provider pricing and usage changes.
• Identify opportunities to reduce unnecessary model and infrastructure expenditure.
• Maintain appropriate fallback and redundancy strategies across AI providers.

Enterprise AI Product Development

• Develop AI solutions for internal business processes and operational teams.
• Convert successful internal AI solutions into scalable products for enterprise customers.
• Work with client-facing and account teams to identify opportunities for adapting AI solutions to existing customer environments.
• Support the deployment of AI products into enterprise client accounts.
• Ensure solutions meet enterprise expectations for reliability, security, compliance, governance, and accuracy.
• Build AI products that can move beyond prototypes into commercially viable solutions.
• Work effectively with enterprise stakeholders throughout product development, implementation, and adoption.

Team Building and Leadership

• Initially operate as a hands-on individual contributor and technical/product leader.
• Determine future team requirements based on platform growth and workload.
• Recruit AI engineers, product professionals, data specialists, and other technical resources as required.
• Establish engineering and product development standards.
• Mentor and develop team members as the AI organization grows.
• Build a culture focused on product quality, technical ownership, experimentation, reliability, and responsible AI development.

Job Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Information Technology, Artificial Intelligence, or a related technical discipline.
• 8+ years of experience in software engineering, AI, technology product development, or a closely related field.
• At least 3 years of hands-on experience working with applied LLM systems.
• Strong practical experience designing, developing, deploying, and maintaining production AI systems.
• Demonstrated experience working with multiple LLM providers and multi-model architectures.
• Experience troubleshooting production AI systems affected by model updates, provider outages, output changes, schema mismatches, or fallback failures.
• Experience managing or directly owning AI inference and technology costs.
• At least 3 years of hands-on experience with agentic AI, tool calling, MCP, or similar technologies.
• Strong understanding of APIs, AI integrations, model routing, agent workflows, and production software architecture.
• Experience building and maintaining web scraping and data acquisition systems.
• Strong understanding of data pipelines, including data extraction, transformation, storage, and integration.
• Practical knowledge of web scraping challenges including rate limiting, changing website structures, headers, proxies, and selector maintenance.
• Understanding of robots.txt, website terms of service, copyright considerations, privacy requirements, and responsible data acquisition.
• Knowledge of GCC data protection and privacy requirements, including relevant considerations under Saudi and UAE regulatory frameworks, is highly desirable.
• Experience working within enterprise security, compliance, governance, and data protection environments.
• Strong understanding of audit logging, human-in-the-loop workflows, data residency, and AI governance.
• Demonstrated experience taking AI products from concept or prototype through production and commercial adoption.
• 10+ years of total product experience, with significant experience in senior product leadership roles such as Head of Product, VP Product, Product Director, or CPO-level responsibilities.

• Experience building products for enterprise customers and managing long enterprise sales and procurement cycles.
• Experience working with enterprise security reviews and compliance requirements.
• Experience managing a portfolio of multiple live products built on shared infrastructure.
• Strong business and product judgment with the ability to prioritize high-value initiatives and reject low-value development work.
• Excellent communication, stakeholder management, problem-solving, and technical leadership skills.

Preferred Technical Skills

Candidates with experience in the following areas will be highly relevant:

• Artificial Intelligence and Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Agentic AI
• AI Agents
• MCP / Model Context Protocol
• Tool Calling
• Multi-Model AI Architecture
• LLM Routing
• AI API Integration
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Grounding and Source Citation
• Web Scraping
• Data Acquisition
• Data Engineering
• API Development
• Enterprise AI
• AI Governance
• AI Security
• Data Privacy
• Data Residency
• Human-in-the-Loop AI
• AI Cost Optimization
• Product Management
• Software Engineering

Salary and Career Insights in Dubai

The advertised salary for this AI Lead position is AED 25,000–30,000 per month, reflecting the seniority and breadth of responsibility involved in the role.

This position combines several high-demand areas—AI engineering, LLM infrastructure, product management, agentic AI, enterprise technology, data engineering, and AI governance. Candidates with proven production experience rather than purely academic or experimental AI exposure may be particularly competitive for similar senior AI leadership positions in the UAE.

Professionals targeting AI leadership roles in Dubai can strengthen their profiles through practical experience in cloud platforms, LLM APIs, AI agent frameworks, data engineering, cybersecurity, enterprise architecture, and responsible AI governance.

Recommended Certifications and Training for AI specialists

Relevant professional development areas may include:

• Generative AI and Large Language Model development
• Machine Learning and Artificial Intelligence
• Cloud AI and Machine Learning certifications
• Data Engineering
• Enterprise Architecture
• Cybersecurity and information security
• AI governance and responsible AI
• Data privacy and protection
• Product management
• Agile and technical product leadership

Career Opportunities – AI

The successful candidate may progress into senior positions such as:

• Head of AI
• Director of Artificial Intelligence
• AI Product Director
• VP of AI Product
• Head of AI Engineering
• Chief AI Officer
• Chief Product Officer
• AI Strategy Director

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