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Applied AI Managertalabat · Dubai, Dubai, United Arab Emirates
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talabat

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Applied AI Manager

🗓️ Posted 2026-10-15 Dubai, Dubai, United Arab Emirates Full-time Hybrid ICT
💰 Salary Not shared 📊 Market AED 30,000–AED 50,000/mo

About the Company

Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat's local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region. We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily. Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner. Job Description About the Applied AI Tribe The Applied AI Tribe is talabat's internal AI engine, a team obsessed with turning cutting-edge AI into genuine, measurable business value. We don't build AI for its own sake. Our north star is Net AI Value Delivered. We have shipped 40+ AI products across four strategic streams: Product & Tech Platform - AI capabilities embedded into talabat's core product and engineering platform. Business Automations - streamlined workflows that save real time and money across operations. Self-Serve AI Tools - empowering talabatis to build and use AI without needing to raise a ticket. Training & Onb

About the Role

As Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists building and shipping the ML and generative AI systems that power decisions across talabat's product and business. You are equally a people leader and a technical practitioner, close enough to the work to make great technical decisions and grow your team, while owning the roadmap and delivery. Your team's work spans the full AI lifecycle: from framing ambiguous business problems through data modelling, feature engineering, model training, deployment, and production monitoring. A significant focus will be leveraging LLMs, agentic systems, and generative AI to automate decisions, enrich data, and build intelligent experiences at scale, all measured against Net AI Value Delivered. What's On Your Plate People Leadership: Lead, grow, and retain a team of data scientists, setting a high bar for technical quality, fostering a culture of ownership, and supporting each person's career development. Partner with recruiting to attract top data science and ML talent as the tribe scales. Mentor team members in ML best practices, agentic system design, production engineering, and stakeholder communication. Run effective team rituals, planning, design reviews, retrospectives, that keep the team aligned and moving with pace. Technical Strategy & Delivery: Translate ambiguous business problems into well-scoped ML and AI solutions with clear, measurable success criteria

Requirements

  • ● What Did We Order
  • ● Technical Experience
  • ● Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining.
  • ● Hands-on knowledge of ML and GenAI frameworks: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
  • ● Proficiency with the OpenAI SDK and familiarity with major LLM provider APIs for building and orchestrating production AI systems.
  • ● Hands-on experience with LangGraph for building stateful, multi-step agentic workflows and complex agent orchestration patterns.
  • ● Solid understanding of embeddings, dense and sparse retrieval, vector databases, and semantic search, for building RAG pipelines and similarity-based applications.
  • ● Strong software engineering fundamentals: clean production code, data structures and algorithms, ML system design.
  • ● Proven experience shipping and monitoring ML models in production, with a solid grasp of MLOps practices.
  • ● Strong data and ML engineering skills: building and orchestrating data pipelines (e.g., Airflow) and robust feature engineering.
  • ● Excellent SQL and Python; solid statistical foundations including experiment design, causal inference, and predictive methods.
  • ● Familiarity with agentic system design (harness-first thinking, LLM observability, evaluation pipelines, and production feedback loops) is a strong plus.
  • ● Bachelor's degree in Engineering, Computer Science, or a related field. A postgraduate degree is a plus.
  • ● + years of experience in data science, ML engineering, and/or generative AI, including shipping models to production.
  • ● + years managing or leading a data science or ML team, with a track record of developing talent and delivering through others.
  • ● Mindset & Ways of Working
  • ● Curiosity over certainty: you're excited to try the new model or framework this week.
  • ● Ownership: you treat cost, latency, and business impact as your problem, and your team's.
  • ● Bias to build: a prototype in a real workflow beats a perfect design doc every time.
  • ● Comfortable with ambiguity: most of what we build has no playbook yet, and you help write it.