Machine Learning Engineer PART TIME

Project description:

The project is a European transport company specialising in airport shuttle services, connecting major airports with nearby cities and regions.

The client is continuously seeking ways to enhance its services through data-driven solutions. One of the latest initiatives focuses on optimizing the timetable generation process. The goal is to create efficient airport transfer bus schedules along with optimized driver and vehicle shift schedules.

Project team: BA, PM, QA, TL, DataScience, FullStack Devs, Devops.

Requirements: 

• Strong knowledge of Operations Research (OR) and Mathematical Optimization techniques.
• Experience with Mixed-Integer Linear Programming (MILP) and constraint-based optimization.
• Hands-on experience with open-source and/or commercial solvers & toolkit like OR-Tools, Gurobi, CPLEX, or Pyomo.
• Strong Python programming skills, especially in writing efficient, production-grade code.
• Experience handling large-scale computations efficiently in a cloud or distributed system.

Nice to have:

• Knowledge of stochastic optimization and heuristic/metaheuristic approaches (Simulated Annealing, Genetic Algorithms).
• Exposure to machine learning for demand forecasting.
• Experience in building and deploying microservices in a production environment.
• Experience with REST APIs, message-driven architectures (Azure Service Bus, RabbitMQ, or Kafka).
• Knowledge of PostgreSQL or other relational databases.
• Experience with Docker & Kubernetes for containerized deployments.
• Understanding of failure handling & retries in asynchronous systems.

Responsibilities:

• Implement a comprehensive mathematical model that captures all constraints and optimization goals.
• Select and justify the best optimization solver based on performance, cost, and business needs.
• Deliver a fully functional solver.
• Provide performance benchmarks and ensure that the solver can handle real-world complexity.