Engineering Manager - Applied AI

About the Role
Since launching in Kuwait in 2004, we have been the leading on-demand food and Q-commerce app, providing convenience and reliability to our customers across eight countries in the region. Our mission is to simplify everyday life through innovative technology, optimizing operations for restaurants and local shops, while offering reliable earning opportunities for our riders.
We are fostering a high-performance culture through an engaged workforce that values making a difference.

About the Applied AI Tribe
The Applied AI Tribe serves as our internal AI engine, dedicated to transforming cutting-edge AI technologies into tangible business value. Our focus is on Net AI Value Delivered as we develop AI products across four strategic streams:

• Product & Tech Platform: Embedding AI capabilities into the core product and engineering platform.
• Business Automations: Streamlining workflows to save time and costs across operations.
• Self-Serve AI Tools: Empowering our employees to utilize AI without needing to raise a ticket.
• Training & Onboarding: Upskilling teams to ensure participation in the AI era.
  The engineering approach emphasizes the deployment of agentic AI systems at scale, incorporating evaluation and feedback loops to drive continuous improvement.

The Role
As the Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists to develop and implement the machine learning (ML) and generative AI systems that facilitate decision-making across our product and business areas. This dual role encompasses both people leadership and technical execution, as you will oversee the roadmap and delivery of your team's work in the AI lifecycle: from solving ambiguous business problems to model training and production monitoring. Your efforts will focus on leveraging large language models (LLMs), agentic systems, and generative AI to enhance decisions, enrich data, and create intelligent experiences at scale.

What's On Your Plate?

People Leadership:

• Lead, grow, and retain a team of data scientists, setting a high technical quality standard and assisting each team member in their career development.
• Collaborate with recruitment teams to attract top data science and ML talent as the tribe expands.
• Mentor team members on ML best practices, agentic system design, and effective stakeholder communication.
• Organize effective team meetings, planning sessions, design reviews, and retrospectives to maintain alignment and productivity.

Technical Strategy & Delivery:

• Convert unclear business challenges into well-defined ML and AI solutions with measurable success criteria in line with the tribe's goals.
• Manage the technical roadmap of the team by prioritizing high-impact projects across data enrichment, business automation, self-serve tools, and product features.
• Promote a harness-first methodology, ensuring that evaluation pipelines and infrastructure are established prior to building agent logic.
• Implement evaluation in every product iteration through golden datasets and aligned metrics that guide development priorities.
• Oversee the entirety of the ML lifecycle, including data pipelines, feature engineering, model training, deployment, and monitoring.
• Propel the use of LLMs and generative AI for data enhancement, smart content understanding, and automated decision-making at scale.
• Design and evaluate experiments to measure the impact of models and products rigorously.
• Raise standards for ML and engineering by improving MLOps, code quality, tools, and internal learning programs.

Cross-functional Partnership:

• Collaborate with product managers and business teams to identify valuable AI opportunities that align with the team's roadmap.
• Communicate effectively with senior stakeholders, from defining problems to presenting results and recommendations.
• Work with engineering teams to ensure reliable data models and smooth integration into production systems.

Qualifications

What Did We Order?
Technical Experience

• Extensive knowledge in machine learning, generative AI, deep learning, natural language processing (NLP), recommendation systems, and data mining.
• Practical experience with ML and generative AI frameworks such as Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, and Transformers.
• Proficient in the OpenAI SDK; familiarity with major LLM provider APIs for production AI systems integration.
• Experience with LangGraph for developing stateful, multi-step workflows and advanced agent orchestration.
• Familiarity with Hugging Face for model sourcing and deployment is advantageous.
• Solid understanding of embeddings, both dense and sparse retrieval methods.
• Strong software engineering skills, including clean production coding practices and ML system designs.
• Proven background in deploying and monitoring ML models with a considerable understanding of MLOps principles.
• Excellent skills in SQL and Python along with a strong statistical foundation for experimental designs and predictive methods.
• Familiarity with agentic system design principles is a plus.
• Experience with BigQuery and Google Cloud Platform is beneficial.

Qualifications

• Bachelor's degree in Engineering, Computer Science, or a related field; a postgraduate degree is a strong plus.
• More than 6 years of experience in data science, ML engineering, or generative AI, including deploying models into production.
• At least 2 years of experience leading or managing a data science or ML team, with a proven track record in developing talent and driving results through others.
• Experience in consumer product settings is highly advantageous.

Mindset & Ways of Working

• Curiosity over certainty: you are eager to explore new models or frameworks.
• Ownership: you consider cost, latency, and impact as part of your responsibility.
• Bias to build: prefer prototyping within a workflow over perfecting documentation.
• Comfort with ambiguity: you are okay with creating new solutions in uncharted territory.
• Keep it simple: choose methods that deliver maximal value with minimal complexity.

Your success is defined by both the results you deliver and the behaviors you exhibit. We value excellence in craftsmanship and adherence to our leadership principles, as these define how we operate and collaborate.