Tech Lead – Logistics Systems

Tech Lead – Logistics Systems at Snoonu

We are seeking an experienced Tech Lead to drive the architecture, delivery, and continuous improvement of AI/ML‑enabled logistics solutions for Snoonu’s Super App. The role blends leadership, systems design, and hands‑on engineering across Python, ML, and cloud platforms.

• Lead a cross‑functional 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, fostering continuous skill growth in software engineering and ML system design.
• Coordinate with Product, Data Science, QA, Dev Ops, and Platform Leads to ensure 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) to ensure 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.
• Collaborate with Data Scientists to product ionize ML models, ensuring reproducibility, versioning and monitoring.
• Lead implementation of model‑serving frameworks, vector databases, data processing jobs and inference optimisation 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 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.

• Bachelor’s degree in Computer Science, Engineering, Data Science or related field.
• Experience in logistics solutions or related domains.
• Advanced degrees (MSc/PhD) in AI/ML or Applied Data Science are a plus.
• Expert‑level Python development with strong understanding of backend frameworks (FastAPI, Flask, Django) and microservices patterns.
• Strong grounding in algorithms, distributed systems and scalable backend architecture.
• Experience deploying ML models to production (batch, real‑time, streaming).
• Knowledge of ML frameworks such as Tensor Flow, PyTorch, Scikit‑learn and model‑serving technologies (Torch Serve, MLflow, Sage Maker, Vertex AI, etc.).
• Experience with vector databases, feature stores and embedding‑based search is a strong plus.
• Hands‑on experience with CI/CD for ML systems, containerisation (Docker), orchestration and cloud environments (AWS/GCP).
• Familiarity with monitoring tools for ML pipelines: logging, metrics, tracing, model drift detection.
• Strong understanding of data pipelines, ETL/ELT workflows and both relational and No
  
  SQL databases.
• Ability to design caching, queuing and event‑driven architectures supporting ML workflows.
• 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 of delegating and of hands‑on deep technical work.
• Comfortable navigating ambiguity and aligning stakeholders in AI‑driven product contexts.
• Establishes testing standards for data validation, model correctness, API reliability and system performance.
• Drives observability across AI services and ensures robust monitoring…