Your AI career in the UAE starts here

About the Project LogiX is an AI and data consulting firm partnering with high-growth companies. We are currently supporting a large, well-established proptech client operating a high-traffic digital marketplace in the real estate sector.

The Role We are hiring a Junior to Mid-level MLOps Engineer (Contractor) to join the delivery team. You will support ongoing machine learning operations, managing the lifecycle of models from development to deployment and ensuring scalable AI infrastructure.

What you'll support / contribute to:

• Supporting the development and maintenance of ML pipelines for model training, testing, and deployment
• Assisting with model deployment, versioning, and containerization (using Docker/Kubernetes)
• Contributing to CI/CD workflows for machine learning and automation scripts under guidance of senior engineers
• Working alongside Data Scientists and Product teams to help ensure model performance and reliability in production
• Participating in documentation and basic model monitoring or governance practices

Details

• Duration: 12-month engagement (potential extension)
• Work Model: Onsite, Remote, or Hybrid (depending on location)

About the Project LogiX is an AI and data consulting firm partnering with high-growth companies. We are currently supporting a large, well-established proptech client operating a high-traffic digital marketplace in the real estate sector.

The Role We are hiring a Junior to Mid-level QA Engineer (Contractor) to join the delivery team. You will support quality assurance initiatives for data, platform, and ML systems.

What youll support / contribute to:

• Supporting QA processes across data pipelines, APIs, and platform components
• Assisting with validation of data accuracy, consistency, and reliability
• Contributing to test case design for ETL jobs, dashboards, and backend services
• Collaborating with engineers to surface issues early and document findings
• Supporting improvements in testing practices and basic automation where applicable

Details

• Duration: 12-month engagement (potential extension)
• Work Model: Onsite, Remote, or Hybrid (depending on location)

About the Project LogiX is an AI and data consulting firm partnering with high-growth companies. We are currently supporting a large, well-established proptech client operating a high-traffic digital marketplace in the real estate sector.

The Role We are hiring a Junior to Mid-level MLOps Engineer (Contractor) to join the delivery team. You will support MLOps initiatives and machine-learning–driven products.

What youll support / contribute to:

• Supporting the deployment, monitoring, and maintenance of machine learning models in production environments
• Assisting with ML pipelines, experiment tracking, and model versioning processes
• Working with data scientists and engineers to help operationalize models
• Contributing to CI/CD, automation, and infrastructure reliability tasks related to ML systems
• Helping monitor model performance and system health over time

Details

• Duration: 12-month engagement (potential extension)
• Work Model: Onsite, Remote, or Hybrid (depending on location)

Job Title: AI Implementation Specialist

Department: Technology / Operations

Reports To: IT HEAD / COO / CEO

Employment Type: Full-Time

About the Role

We are looking for a curious, tech-savvy, and proactive AI Implementation Specialist to help drive the company's AI and automation initiatives. This is a hands-on, execution-focused role where you will work closely with different teams to identify manual, repetitive workflows and transform them using AI-powered tools and automation. No team of your own to manage — just real impact through real projects.

This is a great opportunity for someone early in their career who is passionate about AI and wants to be at the forefront of how businesses use it day-to-day.

Key Responsibilities

AI Project Support & Execution

Assist in the planning, coordination, and execution of AI projects from kick-off to deployment.

Research and evaluate AI tools, platforms, and solutions that fit specific business needs.

Work with vendors and internal stakeholders to integrate AI tools into existing systems and workflows.

Document project progress, timelines, and outcomes to keep stakeholders informed.

Workflow Automation

Conduct walkthroughs with department teams (Finance, Purchasing, Operations, Sales, Logistics, etc.) to understand and map out manual processes.

Identify tasks suitable for automation and propose practical AI-driven solutions.

Build and deploy automations using no-code/low-code tools such as Make (Integromat), Zapier, Microsoft Power Automate, or similar platforms.

Monitor and maintain deployed automations, troubleshooting issues and making improvements over time.

AI Tools Adoption

Help onboard employees onto new AI tools through simple guides, walkthroughs, and hands-on support.

Act as an internal resource and first point of contact for questions related to AI tools in use.

Stay up to date on new AI tools and use cases, and share relevant findings with the team.

Reporting & Documentation

Track key metrics such as time saved, error reduction, and adoption rates for implemented solutions.

Maintain clear documentation of all automations, integrations, and AI workflows.

Prepare simple reports and updates for management on project status and results.

Requirements

Must-Have

1–3 years of experience in a tech, operations, or process improvement role.

Hands-on experience with at least one automation or no-code tool (e.g., Zapier, Make, Power Automate, Notion AI, etc.).

Comfortable using and exploring AI tools (e.g., ChatGPT, Claude, Gemini, or similar).

Strong attention to detail and ability to document processes clearly.

Good communication skills — able to explain tech concepts simply to non-technical colleagues.

Self-starter attitude with a willingness to learn and figure things out.

Nice-to-Have

Exposure to basic scripting or programming (Python, JavaScript) — not required but a plus.

Familiarity with project management tools (Asana, Jira, Notion, Trello).

Experience working across multiple departments or in a cross-functional environment.

Understanding of prompt engineering or working with LLM-based tools.

Success Metrics

Number of manual workflows successfully automated within the first 6–12 months.

Time savings and efficiency gains reported by teams using automated processes.

Employee adoption and satisfaction with AI tools introduced.

Quality and completeness of process documentation maintained.

We're looking for someone who studies humans in their natural AI habitat — part researcher, part product mind, fully obsessed with the gap between what users say they want and what they actually do at 2am with a half-finished prompt.

At ArtisanAI, we don't ship features. We ship intuition made tangible. You'll be the one decoding the silent language of our users — the hesitation before a click, the prompt rewritten three times, the moments where AI feels like magic versus where it falls flat. Then you'll translate those signals into roadmaps that actually ship.

What you'll actually do: Run research that goes deeper than surveys — diary studies, contextual inquiries, prompt-pattern analysis, the works. Sit with users in Dubai cafés, on Zoom with creators in Riyadh, in the inboxes of enterprise buyers in London. Turn raw human behaviour into product bets the team can rally behind. Own discovery end-to-end, then steward features from insight to launch.

You probably are: Someone who thinks in stories and ships in sprints. Fluent in the vocabulary of LLMs without losing the plot of why any of it matters to a real person. Strong opinions on the gap between AI demos and AI usefulness — and the receipts to back them up.

Experience: 5+ years minimum — and we want to see the old-school reps. Years spent running real user interviews before AI summarised them for you. Roadmaps shaped without ChatGPT writing the PRD. Insights earned through screen-shares, post-it walls, and uncomfortable silences in user sessions. The new tools are welcome — but we're hiring the instincts you built before they existed.

Where: Dubai. In-person, mostly. The best products come from teams who break bread, argue at whiteboards, and occasionally watch the sunset from the office.

We are seeking a forward-thinking Senior Manager of Artificial Intelligence who has successfully transitioned beyond traditional machine learning to architect and deploy modern Generative AI and Agentic systems. You will not just be managing models; you will be building autonomous ecosystems. We need a hands-on leader who has a proven track record of taking complex LLM-driven solutions from conceptual wrappers to robust, production-grade agentic workflows that drive measurable enterprise value.What You’ll DoDrive Next-Gen Strategy: Own the enterprise AI roadmap with a heavy emphasis on Generative AI, autonomous agents, and applied Large Language Models.Orchestrate Agentic Ecosystems: Lead the design and deployment of multi-agent systems, orchestrating specialized AI agents (e.g., intelligence routing, operational execution, and system coding tasks) governed by centralized monitoring dashboards.Ship to Production: Guide cross-functional engineering teams to move GenAI proofs-of-concept into reliable, scalable, and secure production environments.Champion LLMOps: Establish the infrastructure required for modern AI, including advanced RAG (Retrieval-Augmented Generation) pipelines, vector databases, and continuous agent evaluation.Ensure AI Governance: Implement strict guardrails for Generative AI, focusing on algorithmic transparency, data privacy, security, and prompt injection mitigation.What You BringBackground: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related highly technical field.Experience: 15+ years in technology or engineering roles, with recent, dedicated leadership experience explicitly focused on deploying Generative AI and Agentic solutions into production.Modern AI Expertise: Deep, practical knowledge of LLM architectures, fine-tuning methodologies, and multi-agent orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI).Infrastructure Acumen: Strong understanding of the modern AI stack, including vector databases, cloud-native deployments, and LLMOps practices.Leadership: Proven ability to demystify complex GenAI concepts for executive stakeholders, secure buy-in, and mentor high-performing teams of modern AI engineers.