Tech Lead – Logistics Systems

About the Role

Join Snoonu as a Tech Lead overseeing engineering efforts for Python-based logistics systems integrating AI/ML and data-driven platforms. This role entails guiding a multidisciplinary team, designing scalable system architectures, and delivering robust AI/ML solutions aligned with the company's strategic ambitions.

Key Responsibilities

• Lead a diverse engineering team comprising senior to junior developers focused on Python applications, AI/ML services, and data platforms.
• Mentor engineers across different experience levels to uphold engineering standards and fulfill AI/ML project goals.
• Establish and track short, medium, and long-term milestones while fostering professional growth in software and ML system design.
• Collaborate closely with cross-functional teams including Product, Data Science, QA, DevOps, and Platform leads to ensure smooth delivery of AI/ML projects.
• Drive end-to-end architecture and design for AI/ML solutions, including Python backends, model-serving infrastructures, and scalable deployment mechanisms such as REST APIs, streaming, and batch processing.
• Define standards for feature engineering pipelines, data ingestion, and integration with MLOps environments.
• Decompose complex AI/ML initiatives into executable tasks and formulate clear technical roadmaps.
• Partner with Data Scientists to productionize and monitor ML models, ensuring reproducibility and version control.
• Lead implementation of model-serving frameworks, vector databases, data processing workflows, and inference optimization.
• Enforce best practices in experiment tracking, model evaluation, and A/B testing for ML functionalities.
• Enhance development workflows, CI/CD pipelines, and automate ML lifecycle processes.
• Promote adherence to coding standards, thorough technical documentation, and reduction of technical debt within AI/ML projects.
• Introduce novel tools and technologies to improve model performance, data integrity, and engineering productivity.
• Participate actively in recruiting backend and ML engineers.
• Conduct knowledge-sharing sessions on ML system architecture, Python best practices, and cloud-native development techniques.
• Foster a collaborative team culture emphasizing mentorship and effective communication.
• Ensure reliability of AI/ML services by overseeing monitoring of model drift, data quality, and performance metrics.
• Manage incident responses, root cause analyses, and coordinate cross-team escalations related to production ML systems.

Qualifications and Skills

• Bachelor's degree in Computer Science, Engineering, Data Science, or related discipline required; advanced degrees such as MSc or PhD in AI/ML or applied data science are advantageous.
• Experience within logistics or closely related domains is essential.
• Exceptional communication abilities to simplify complex ML workflows and architectural choices for diverse stakeholders.
• Data-driven decision-making aptitude, capable of justifying improvements using relevant metrics.
• Leadership balance combining delegation skills with hands-on technical expertise.
• Adeptness in navigating ambiguous scenarios and aligning stakeholders around AI-driven products.
• Expert proficiency in Python development and backend engineering with frameworks such as FastAPI, Flask, and Django, plus sound knowledge of microservices architecture.
• Strong background in algorithms, distributed computing, and scalable backend architecture design.
• Experience deploying ML models in production environments supporting batch, real-time, and streaming workflows.
• Familiarity with ML frameworks including TensorFlow, PyTorch, Scikit-learn, and model-serving tools like TorchServe, MLflow, SageMaker, or Vertex AI.
• Knowledge of vector databases, feature stores, and embedding-based search technologies is a strong plus.
• Hands-on experience with MLOps ecosystems, including CI/CD pipelines, containerization (Docker), orchestration, and cloud platforms such as AWS or GCP.
• Experience with monitoring ML pipelines encompassing logging, metrics, tracing, and model drift detection.
• Deep understanding of data pipelines, ETL/ELT processes, relational and NoSQL databases, and design of caching, queuing, and event-driven architectures for ML workflows.
• Ability to establish testing standards for data validation, model accuracy, API reliability, and system performance.
• Commitment to driving observability and comprehensive monitoring across AI services.

Company Overview: Snoonu

Snoonu stands as Qatar's indigenous Super App, transforming everyday living through speedy delivery, shopping, and diverse services within a unified platform. Powered by an international team and technology-first mindset, the company strives to influence the region through continuous innovation.

Company Values

• Customer obsession: placing customer needs as the highest priority.
• Integrity: practicing honesty, ethics, and trustworthiness consistently.
• Creativity and curiosity: fostering innovation for impactful solutions.
• Ownership and leadership: leading by example and embracing accountability.
• Efficiency: working smarter to achieve better and faster outcomes.
• Teamwork: valuing collaboration and collective strength.

Benefits and Work Culture

• Global collaboration opportunities within a diverse, international team.
• Continuous learning with budgets for courses and growth tools.
• Autonomy to own tasks and pathways for development.
• Flexible leave policies promoting wellness and recharging.
• Agile methodologies actively practiced across teams.
• Great Place to Work® certification indicating a people-first culture.
• ISO 9001:2015 and ISO 45001:2018 certified ensuring quality and safety standards.
• Commitment to sustainability, social responsibility, and inclusivity.