A leading technology-driven organisation in Dubai, UAE is hiring a senior Artificial Intelligence Engineer to design, build, and deploy cutting-edge AI agent platforms, self-hosted Large Language Model (LLM) environments, and enterprise-grade MLOps pipelines. This is a high-impact, hands-on engineering role for a proven AI/ML professional who can translate complex business objectives into scalable, production-ready AI solutions — all within a secure, private, and self-hosted infrastructure. If you have 8+ years of deep AI/ML engineering experience and thrive on building intelligent systems that drive real business outcomes, this Dubai opportunity is made for you.
About the Role — AI Engineering at the Frontier
Core Focus: Design and deploy self-hosted LLM environments (LLaMA, Mistral) with no reliance on external cloud AI APIs
AI Platform Scope: End-to-end MLOps ownership — deployment, monitoring, model versioning, performance optimisation, continuous improvement
Security Architecture: Data isolation, encryption, RBAC, SSO, access controls, audit logging, and governance frameworks
Enterprise Integration: Microsoft OneDrive, Jira, GitLab, repositories, and productivity tools for seamless cross-functional automation
Team Impact: Technical leadership, mentorship of junior engineers, and cross-functional stakeholder collaboration
Career Opportunity — Dubai’s Growing AI Ecosystem
Strategic Location: Dubai, UAE — one of the world’s fastest-growing hubs for AI and technology innovation
Seniority Level: Senior AI Engineering role with architecture ownership and commercial client-facing responsibilities
Technology Stack: Python, Docker, Kubernetes, LLaMA, Mistral, Power BI, ERP, MLOps frameworks
Business Impact: AI solutions spanning workflow automation, analytics, reporting, decision-making, and business ROI measurement
Position Overview
The Artificial Intelligence Engineer in Dubai partners with the Product Manager to architect, develop, and deliver secure AI agent platforms that automate business operations across multiple entities. Reporting into senior technical leadership, this role owns the complete AI platform lifecycle — from initial design through self-hosted LLM deployment, containerised environment management, integration with enterprise tools, and ongoing performance optimisation. You will design AI agent ecosystems supporting engineering workflows, business analytics, and decision-making, while ensuring all solutions meet strict internal data protection and security compliance standards. This role also carries a commercial dimension — supporting client engagements, solution demonstrations, and technical consultations as part of the organisation’s business development efforts.
Why This Role Matters: As an Artificial Intelligence Engineer in Dubai, you sit at the very centre of enterprise AI transformation — building self-hosted LLM platforms, MLOps pipelines, and AI agent ecosystems that deliver measurable business ROI. You will lead architecture decisions, mentor junior engineers, integrate AI solutions with enterprise platforms including Microsoft OneDrive, Jira and GitLab, and translate technical innovation into commercial value — all in one of the world’s most dynamic and AI-forward cities.
Key Responsibilities
AI Agent Platform Design & Development
- Partner with the Product Manager to design, develop, and implement secure, private AI agent platforms and supporting portals that automate business operations across multiple entities
- Design and develop AI agent ecosystems supporting engineering processes, workflow automation, business operations, reporting, analytics, and executive decision-making
- Build scalable, modular, and reusable AI solutions deployable across multiple business units and diverse use cases
- Apply an iterative, experimentation-driven development approach — validating assumptions, measuring outcomes, and continuously refining AI performance
Self-Hosted LLM Deployment & MLOps
- Architect, deploy, and manage self-hosted Large Language Model environments including LLaMA and Mistral — entirely without reliance on external or cloud-based AI APIs
- Own the end-to-end AI platform lifecycle (MLOps) covering deployment, monitoring, model versioning, performance optimisation, and continuous improvement
- Implement and manage containerised and orchestrated environments using Docker and Kubernetes for reliable, scalable AI infrastructure
- Evaluate emerging AI technologies, frameworks, and architectures, translating cutting-edge innovations into practical, production-ready business solutions
Secure AI Architecture & Governance
- Design and maintain secure AI architectures incorporating data isolation, encryption, access controls, audit logging, and comprehensive governance best practices
- Develop and support internal applications and user portals with Single Sign-On (SSO), Role-Based Access Control (RBAC), and user management capabilities
- Ensure all AI solutions comply fully with internal data protection and security policies across all business entities
Enterprise Integration & Automation
- Integrate AI solutions with enterprise platforms including Microsoft OneDrive, Jira, GitLab, code repositories, and productivity tools to enable seamless cross-functional automation
- Build monitoring, reporting, and feedback mechanisms to measure agent performance, user adoption, operational efficiency, and business ROI
- Develop internal applications, portals, and tooling that bring AI capabilities directly into everyday business workflows
Technical Leadership, Mentorship & Commercial Support
- Provide technical leadership and mentorship to junior engineers while independently managing complex, high-stakes technical initiatives
- Promote knowledge sharing by documenting AI best practices, reusable patterns, and lessons learned to strengthen engineering capabilities across the organisation
- Support commercial and client-facing activities including technical solution guidance, customer demonstrations, and maintaining technical relationships with clients
- Communicate technical strategies, progress, risks, and trade-offs effectively to both technical and non-technical stakeholders, keeping engineering efforts aligned with business goals
Qualifications & Requirements
Educational Requirements
- Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a closely related technical discipline
Experience Requirements
- Minimum 8 years of hands-on AI/ML engineering experience, ideally within technology-driven organisations or fast-paced product environments
- Proven track record developing, deploying, and optimising AI/ML solutions in real-world, production-grade business environments
- Demonstrated ability to balance strategic thinking with practical, hands-on execution — identifying long-term AI opportunities while delivering scalable solutions today
Technical Skills
- Strong, production-level expertise in Python for AI/ML development and engineering workflows
- Hands-on experience with self-hosted LLM environments (LLaMA, Mistral) and MLOps frameworks for model deployment and lifecycle management
- Proficiency with containerisation and orchestration technologies including Docker and Kubernetes
- Strong analytical and data-driven mindset with deep understanding of structured, enriched, high-quality data in building effective AI systems
Soft Skills & Mindset
- Highly collaborative, proactive team player with genuine passion for knowledge sharing, continuous improvement, and collective success
- Comfortable operating in dynamic, rapidly evolving environments where innovation, experimentation, and execution are equally valued
- Strong problem-solving, communication, and stakeholder management skills — translating complex technical concepts into clear business value
About the Hiring Company
This opportunity is with a technology-driven organisation operating across multiple business entities in Dubai, UAE. The company is actively building out its AI engineering capability — investing in self-hosted AI infrastructure, private LLM platforms, and intelligent automation systems that deliver real, measurable business value. The AI Engineering team works at the intersection of technology leadership and commercial impact, partnering closely with Product Managers, internal stakeholders, and external clients to deliver AI solutions that genuinely transform operations.
Culture & Environment: Fast-paced, innovation-driven, and deeply collaborative. This is a team that values experimentation, owns outcomes, and builds things that matter — without shortcuts.
Who Should Apply?
- Senior AI/ML Engineers: With 8+ years experience in production AI systems, LLM deployment, and MLOps seeking a senior role in Dubai’s tech sector
- LLM & Generative AI Specialists: Experienced with self-hosted models (LLaMA, Mistral) and secure AI platform architecture in enterprise environments
- MLOps Engineers: With end-to-end platform ownership experience including Docker, Kubernetes, model versioning, and performance monitoring
- AI Platform Architects: Who combine deep technical depth with the ability to lead teams, mentor junior engineers, and communicate to business stakeholders
- Python-First AI Engineers: With strong backgrounds in data-driven AI development, enterprise integration, and iterative, experimentation-led solution building
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