Applied AI Manager

Job Title: Applied AI Manager Company: Delivery Hero Job Category: Machine Learning Roles & Responsibilities: 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. Job Description About the Applied AI Tribe 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. 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. * Translate ambiguous business problems into well\-scoped ML and AI solutions with clear, measurable success criteria tied to the tribe's north star. * Own the team's technical roadmap: prioritise high\-impact work across data enrichment, business automation, self\-serve tooling, and agentic product features. * Champion harness\-first thinking, ensure evaluation pipelines, LLM observability, tooling, and infrastructure are in place before agent logic is built on top. * Embed evaluation into every product iteration: golden datasets, stakeholder\-aligned metrics, and weekly eval jobs that guide what gets built, fixed, or retired. * Oversee the full ML lifecycle: data pipelines, feature engineering, model training, production deployment, serving, and monitoring. * Drive adoption of LLMs and generative AI for data enrichment, smart content understanding, and automated decision\-making at scale. * Design and analyse experiments (A/B and multivariate) to rigorously measure model and product impact. * Elevate ML and engineering standards across the team, improving MLOps, code quality, tooling, and internal learning programmes. Cross\-functional Partnership:* Partner with product managers and business teams to identify high\-value AI opportunities and shape them into the team's roadmap. * Communicate clearly with senior stakeholders, from problem framing through to results and recommendations. * Collaborate with engineering teams to understand data systems, build reliable data models, and ensure smooth production integration. Work Location: Dubai, DU, United Arab Emirates Availability: Full time

Qualifications 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. * Experience with Hugging Face (Transformers, Hub, Inference API) for model sourcing, fine\-tuning, and deployment. * 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. * Experience with BigQuery and Google Cloud Platform is a plus. Qualifications * Bachelor's degree in Engineering, Computer Science, or a related field. A postgraduate degree is a plus. * 6\+ years of experience in data science, ML engineering, and/or generative AI, including shipping models to production. * 2\+ years managing or leading a data science or ML team, with a track record of developing talent and delivering through others. * Experience building ML systems in an online consumer product environment is a strong plus. Key Skills: Machine Learning, Python, TensorFlow, PyTorch, Model Training, Feature Engineering, NLP Salary: Not disclosed