AI Engineering Intern

🧑‍🏫 Active coaching from the engineering and product teams, who are deeply invested in your growth, skill development, and resume building.

👀 Direct exposure to how a product team builds and ships AI features in a production environment.

💪 Real ownership over code , models, and systems that the business depends on.

📄 A portfolio of shipped work - models, pipelines, and features you can point to.

🤜🤛 Cross-functional experience working with Product, Engineering, and Customer Success.

Location : Dubai, onsite

Duration : 3 months

Start date : 1 September 2026

Compensation : Unpaid - see what we're offering above

Build and iterate on AI-powered tools that help real estate customers discover properties, qualify their requirements, and connect with the right agent.

Develop LLM-driven conversational workflows that understand natural-language enquiries, maintain context, call business tools, and guide customers through rental and sales journeys.

Integrate property listings, customer records, lead-management systems, and internal APIs to provide accurate, relevant, and personalized responses.

Improve prompts, qualification logic, property recommendations, and automated lead updates through structured evaluation, conversation reviews, and production feedback.

Build and maintain reliable APIs, data pipelines, and microservices that support real-time conversations and synchronize customer and property data across systems.

Monitor production performance, investigate failed or suspicious conversations, and improve safeguards, observability, testing, and fallback behavior.

Work closely with product and engineering teams to turn real estate workflows into clear technical solutions, while exploring relevant advances in LLMs, agentic systems, and conversational AI.

Strong Python fundamentals - you're comfortable writing clean, readable code and picking up new libraries quickly. You'll demonstrate this in the application task.

Curiosity about how AI systems actually work in production , not just in notebooks. You care about latency, reliability, and what happens when a model gets it wrong.

Comfort sitting with ambiguity until it resolves - you ask basic questions until something actually makes sense, and you don't move on until it does. That's a feature of this role, not a gap.

Attention to detail - a model that's 90% right in the wrong direction is a model that erodes trust. You notice what's off.

Some exposure to NLP or LLM tooling (OpenAI, Anthropic, LangChain, or similar) is a strong plus, even through side projects or coursework.

Currently pursuing or recently completed a degree in Computer Science, Data Science, Mathematics, or a related field.

If you read this and saw yourself in it - or just found it genuinely interesting - hit the Apply button. 🚀