Ecomitize

Company introduction
FliteTransport is a transportation and logistics company focused on local delivery of heavy and non standard freight, shipments that major carriers often avoid or price very high. The company is building a scalable software platform that combines dispatcher operations, driver mobile workflows, realtime delivery visibility, and optimization technology to improve routing, on time performance, and cost efficiency. The goal is a product grade solution that can scale across regions and be offered to multiple carriers in different markets.

About the role
This role focuses on building and scaling the backend of a logistics optimization platform. The stack uses Django and Django REST Framework for APIs, Celery and Redis for background processing, Angular for the web portal, and an Android driver app. The platform will run on Google Cloud Run and will integrate Google OR-Tools for deterministic route optimization. Travel times and distances will be sourced from Google Maps Platform APIs (or equivalent mapping providers) and combined with operational constraints such as time windows, service durations, and pickup plus delivery pairing. The system also supports realtime driver and delivery tracking for dispatcher visibility and customer updates, and will gradually add AI driven decision support using telemetry and route outcome data.

Key responsibilities

• Own backend architecture and implementation for Django REST APIs across shipments, pickups and deliveries, routes, drivers, vehicles, hubs, and service zones
• Build and scale realtime delivery tracking, including driver location ingestion, websocket or event fanout, dispatcher views, and multi instance correctness
• Refactor and harden Celery based background pipelines for bulk imports, route creation, and optimization jobs, including queue design, retries, idempotency, and monitoring
• Lead integration of Google OR-Tools into production routing workflows, including time windows, service durations, vehicle capacity constraints, and pickup plus delivery pairing
• Integrate mapping services, such as Google Maps Distance Matrix, Directions, Geocoding, and Places where needed, and define caching strategies for cost and performance
• Define event and telemetry schemas that support analytics, explainable dispatch workflows, and future AI decision support
• Support cloud deployment patterns on Google Cloud Run, including container strategy, configuration, secrets management, and operational observability
• Mentor developers and establish engineering standards for code quality, security posture, and deployment reliability

Required skills

• Strong Python and deep Django experience, including Django REST Framework, ORM performance, migrations, and authentication patterns
• Production experience with Celery and Redis or equivalent queue systems, including task design, retries, idempotency, and scaling
• Strong relational database skills, PostgreSQL preferred, including query tuning, indexing, and transactional consistency
• Experience designing versioned APIs for web and mobile clients, with attention to backward compatibility and stable contracts
• Experience building realtime backend features, for example websockets, pubsub, or event streaming patterns
• Experience deploying containerized services, including Docker and CI CD workflows
• Strong security fundamentals, including token based auth, CORS and host restrictions, secrets handling, and audit logging

Nice to have

• Experience with Google Cloud Run, Cloud SQL, Cloud Storage, Pub/Sub, Secret Manager, and monitoring tooling
• Experience with Django Channels and Redis based channel layers for realtime tracking
• Experience integrating Google Maps Platform APIs or equivalent mapping services, including rate limiting, caching, and cost control
• Familiarity with optimization concepts and routing, especially VRP or VRPTW, and practical interest in Google OR-Tools
• Experience with geospatial concepts, PostGIS, or GeoPandas
• Experience building analytics or feature pipelines for AI readiness
• Experience using LLM services such as ChatGPT or Gemini for structured extraction and operational decision support, without treating LLMs as the routing solver