Machine Learning

About The Opportunity

• Design and implement end-to-end ML solutions for forecasting, personalization, and anomaly detection—data ingestion to model deployment and monitoring.
• Develop robust feature engineering pipelines and train models using Python and modern ML frameworks (PyTorch/TensorFlow).
• Deploy models to production with containerized services, CI/CD pipelines, and orchestration for scalable inference (Docker, Kubernetes or cloud managed services).
• Collaborate with product, data, and backend teams to integrate ML outputs into booking flows, pricing engines, and recommendation surfaces.
• Establish evaluation metrics, run A/B tests, and set up monitoring/alerting for model performance and data drift.
• Document best practices, participate in code reviews, and mentor junior engineers on ML engineering and MLOps standards.
  

Skills & Qualifications

Must-Have

• 3+ years of hands-on experience building and deploying ML models in production using Python and PyTorch or TensorFlow.
• Strong experience in feature engineering, time-series forecasting or recommender systems, and practical model evaluation.
• Familiarity with cloud platforms (AWS/GCP/Azure), containerization (Docker) and CI/CD for ML workflows.
• Solid SQL skills and experience working with large datasets; good software-engineering practices (Git, automated tests).
  

Preferred

• Experience with MLOps tools (MLflow,Seldon,TFX) and model monitoring/dataset-drift tooling.
• Domain experience in travel/tourism: dynamic pricing, demand forecasting, personalization or related systems.
  

• Remote-first role with flexible working hours and a high-autonomy engineering culture.
• Opportunity to impact core revenue streams—work closely with product and commercial teams to ship measurable results.
• Learning & development support, regular cross-functional collaboration, and a focus on pragmatic, production-ready ML.