Tech Lead – Logistics Systems

🚀 Welcome to Your Next Adventure! 🛠 What You’ll Get Your Hands On Team Leadership • Lead a cross-functional engineering team focused on Python applications, AI/ML services, and data-driven platforms. • Mentor Senior, Middle, and Junior Engineers, ensuring alignment with engineering standards and ML project requirements. • Set short-, mid-, and long-term goals for the team, continuously supporting skill growth in software engineering and ML system design. • Coordinate with Product, Data Science, QA, DevOps, and Platform Leads to ensure the successful delivery of AI/ML initiatives. Architecture & System Design • Drive end-to-end architecture and design for AI/ML applications, model-serving infrastructure, and Python backend services. • Oversee architectural decisions for scalable model deployment (REST APIs, streaming pipelines, batch systems), ensuring performance, reliability, and maintainability. • Define standards for feature engineering pipelines, data ingestion flows, and integration with MLOps platforms. • Break down complex AI/ML initiatives into actionable engineering tasks and clear technical roadmaps. AI/ML System Delivery • Collaborate with Data Scientists to productionize ML models, ensuring reproducibility, versioning, and monitoring. • Lead implementation of model-serving frameworks, vector databases, data processing jobs, and inference optimization strategies. • Ensure adherence to best practices in experiment tracking, model evaluation, and A/B testing for ML features. Process Improvement & Engineering Excellence • Improve development workflows, CI/CD pipelines, and ML lifecycle automation. • Promote coding standards, technical documentation, and reduction of technical debt across AI/ML projects. • Introduce new tools and technologies that enhance model performance, data quality, and engineering productivity. Recruitment, Culture & Knowledge Sharing • Participate in hiring for both backend and ML-focused engineering roles. • Facilitate knowledge-sharing sessions around ML system architecture, Python best practices, and cloud-native development. • Strengthen team culture through collaboration, mentorship, and proactive communication. Incident Management & Reliability • Oversee reliability of AI/ML services, including model drift detection, data quality monitoring, and performance metrics. • Coordinate incident response, root-cause analysis, and cross-team escalations for production ML systems. 🧙‍♂️ The Magic You Bring • Bachelor’s degree in Computer Science, Engineering, Data Science, or related field. • Experience in logistics solutions or related fields. • Advanced degrees (MSc/PhD) in AI/ML or Applied Data Science are a plus. • Strong communication skills, able to explain complex ML workflows and architectural decisions to technical and business stakeholders. • Data-driven decision-making and the ability to justify improvements using metrics. • Balanced leadership mindset—capable both of delegating and of hands-on deep technical work. • Comfortable navigating ambiguity and aligning stakeholders in AI-driven product contexts. Python & Backend Engineering • Expert-level Python developer with strong understanding of backend frameworks (FastAPI, Flask, Django) and microservices patterns. • Strong grounding in algorithms, distributed systems, and scalable backend architecture. AI/ML Engineering • Experience deploying ML models to production (batch, real-time, streaming). • Knowledge of ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and model-serving technologies (like TorchServe, MLflow, SageMaker, Vertex AI, etc.). • Experience with vector databases, feature stores, and embedding-based search is a strong plus. MLOps & Cloud • Hands-on experience with CI/CD for ML systems, containerization (Docker), orchestration, and cloud environments (AWS/GCP). • Familiarity with monitoring tools for ML pipelines: logging, metrics, tracing, model drift detection. Data, Storage & Integration • Strong understanding of data pipelines, ETL/ELT workflows, and both relational and NoSQL databases. • Ability to design caching, queuing, and event-driven architectures supporting ML workflows. Quality & Reliability • Establishes testing standards for data validation, model correctness, API reliability, and system performance. • Drives observability across AI services and ensures robust monitoring coverage. Inside Snoonu’s Universe Snoonu is Qatar’s homegrown Super App, reinventing daily life with blazing-fast delivery, shopping, and more – all in one place. Powered by tech, driven by a global team, and obsessed with making life easier. The Dream We’re Chasing To be the first Qatari Ultra App that propels the region and its community through innovation and technology. We have global ambitions where what we do surpasses norms and limitations every time. The Quest We’re On To radically transform how people live by leveraging technology to connect them with endless possibilities. Our Everyday Superpowers 👥 Be Customer Obsessed: “Focus on the customer and all else will follow.” 💪 Act with Integrity: “We are honest, ethical, and trustworthy in everything we do.” 🧪 Be Curious and Creative: “We constantly innovate and create solutions to bring a lasting positive impact.” 🏅Lead by Example and Take Ownership: “Be the change you want to see and take ownership.” 🚀 Work Smart and Deliver Results: “You can do more by doing less, better, and faster.” 👨‍💼 It's All About People: “Be a team player; together we are stronger.” Perks & Worklife Magic At Snoonu 🌐 Global Vibes – Collaborate with a worldwide crew. 🧠 Brain Boosters – Learning budgets, access to courses, and tools for your growth. 🚒 Builder’s Playground – Own your tasks, own your path! We’re big on autonomy. 🏝️ Flexible Time Off – We take recharging seriously. Generous leave and wellness policies. 🛠️ Agile Everything – Scrum isn’t a buzzword here. It’s how we roll, from product to ops. Great Place to Work® Certified We’re certified as a Great Place to Work®, a recognition that celebrates a culture we’ve built together where people come first, always. This certification reflects our commitment to creating a workplace where ever

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field. Experience in logistics solutions or related fields. Advanced degrees (MSc/PhD) in AI/ML or Applied Data Science are a plus. Strong communication skills, able to explain complex ML workflows and architectural decisions to technical and business stakeholders. Data-driven decision-making and the ability to justify improvements using metrics. Balanced leadership mindset—capable both of delegating and of hands-on deep technical work. Comfortable navigating ambiguity and aligning stakeholders in AI-driven product contexts.

Lead a cross-functional engineering team focused on Python applications, AI/ML services, and data-driven platforms. Mentor Senior, Middle, and Junior Engineers, ensuring alignment with engineering standards and ML project requirements. Set short-, mid-, and long-term goals for the team, continuously supporting skill growth in software engineering and ML system design. Coordinate with Product, Data Science, QA, DevOps, and Platform Leads to ensure the successful delivery of AI/ML initiatives. Drive end-to-end architecture and design for AI/ML applications, model-serving infrastructure, and Python backend services. Oversee architectural decisions for scalable model deployment (REST APIs, streaming pipelines, batch systems). Define standards for feature engineering pipelines, data ingestion flows, and integration with MLOps platforms. Break down complex AI/ML initiatives into actionable engineering tasks and clear technical roadmaps. Collaborate with Data Scientists to productionize ML models, ensuring reproducibility, versioning, and monitoring. Lead implementation of model-serving frameworks, vector databases, data processing jobs, and inference optimization strategies. Ensure adherence to best practices in experiment tracking, model evaluation, and A/B testing for ML features. Improve development workflows, CI/CD pipelines, and ML lifecycle automation. Promote coding standards, technical documentation, and reduction of technical debt across AI/ML projects. Introduce new tools and technologies that enhance model performance, data quality, and engineering productivity. Participate in hiring for both backend and ML-focused engineering roles. Facilitate knowledge-sharing sessions around ML system architecture, Python best practices, and cloud-native development. Strengthen team culture through collaboration, mentorship, and proactive communication. Oversee reliability of AI/ML services, including model drift detection, data quality monitoring, and performance metrics. Coordinate incident response, root-cause analysis, and cross-team escalations for production ML systems.