Tech Lead – Logistics Systems

About the Role

Join Snoonu as a Tech Lead overseeing a multidisciplinary engineering team focused on Python-based backend services, AI/ML integrations, and data-powered platforms to drive logistics innovations.

Key Responsibilities

• Head a cross-functional team including Senior, Middle, and Junior engineers, aligning efforts with engineering standards and machine learning project goals.
• Set clear short-, mid-, and long-term objectives for the team, fostering skill development in software engineering and ML system design.
• Collaborate closely with Product, Data Science, QA, DevOps, and Platform leadership to ensure successful execution of AI/ML deliverables.
• Lead end-to-end architectural design for AI/ML applications, including model-serving infrastructure and Python backends, ensuring scalability, reliability, and maintainability.
• Establish pipelines for feature engineering, data ingestion, and integration with MLOps platforms, and decompose complex projects into actionable engineering tasks with defined technical roadmaps.
• Work alongside Data Scientists to productionize ML models with proper reproducibility, version control, and monitoring; guide implementation of model-serving frameworks, vector databases, data processing, and inference optimization.
• Promote best practices in experiment tracking, model evaluation, and A/B testing related to machine learning features.
• Drive improvements in CI/CD workflows, ML lifecycle automation, coding standards, technical documentation, and reduction of technical debt within AI/ML projects.
• Identify and integrate new technologies that enhance model performance, data quality, and engineering efficiency.
• Contribute to recruitment efforts for backend and ML engineering roles and facilitate knowledge sharing on ML architecture, Python development, and cloud-native solutions.
• Enhance team culture through mentorship, communication, and collaboration.
• Oversee service reliability concerning AI/ML including monitoring for model drift, data quality, and performance metrics; coordinate incident management and root-cause analysis across teams.

Qualifications and Skills

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related disciplines; advanced degrees in AI/ML or Applied Data Science are advantageous.
• Experience in logistics solution domains is required.
• Strong proficiency in Python programming with deep knowledge of backend frameworks such as FastAPI, Flask, and Django, and microservices architecture.
• Solid foundation in algorithms, distributed systems, and designing scalable backend architectures.
• Proven track record in deploying machine learning models to production in batch, real-time, or streaming contexts.
• Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and model-serving tools like TorchServe, MLflow, SageMaker, or Vertex AI.
• Knowledge of vector databases, feature stores, and embedding search technologies is a plus.
• Hands-on experience in MLOps practices, including CI/CD pipelines, containerization with Docker, orchestration, and cloud platforms (AWS or GCP).
• Expertise in monitoring ML pipelines covering logging, metrics, tracing, and model drift detection.
• Understanding of data engineering concepts including design of ETL/ELT pipelines, relational and NoSQL databases, caching, queuing, and event-driven systems supporting ML workflows.
• Capability to implement rigorous data validation, model correctness tests, API reliability checks, and performance monitoring, fostering observability across AI services.
• Strong communication skills to effectively convey technical and ML workflow details to stakeholders.
• Balanced leadership style that combines delegation with hands-on technical contribution and the ability to manage ambiguity in AI-driven product environments.
• Data-driven mindset for making informed decisions supported by metrics.

Company Culture and Perks

Snoonu is Qatar’s indigenous super app redefining everyday life through rapid delivery and shopping options, driven by a global team with a passion for technology and innovation. The organization values customer focus, integrity, creativity, ownership, smart working, and teamwork.

Employees benefit from a collaborative global environment, learning budgets with access to courses and tools, high autonomy with ownership of tasks, flexible and generous leave policies for work-life balance, and agile methodologies embedded in operations. Snoonu is Great Place to Work® certified, holds ISO 9001:2015 and ISO 45001:2018 certifications reflecting quality and workplace safety commitments, and demonstrates strong corporate social responsibility and diversity inclusion practices.

Additional Information

This role entails onsite work in Qatar for a full-time employment period. Snoonu actively supports sustainable practices and community engagement initiatives.