backend engineer with MLOps for travel ML systems

Parser is a global leader in the travel industry. Its Machine Learning Engineering team builds ML solutions for ranking, recommendation, and pricing optimization.

• Design and implement scalable ML infrastructure for training, deployment, and serving in batch and real-time environments
• Build and maintain efficient data pipelines for large-scale processing and feature engineering
• Optimize compute resources and improve model-serving performance across ML systems
• Implement monitoring, logging, and alerting systems, and contribute to ML Ops practices, including CI/CD pipelines
• Collaborate with data scientists to streamline the model development-to-production workflow
• Research and integrate new technologies
• Mentor junior engineers and communicate technical solutions to diverse stakeholders

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field
• 6–7 Years of experience in Software, Data, or ML engineering roles (preferred)
• Strong problem-solving skills
• Proficiency in Java; Python is desirable
• Understanding of ML algorithms and model architectures
• Experience building scalable, reliable ML systems
• Exposure to cloud platforms such as AWS, containerization with Docker, and scalable data systems such as Spark and Kafka
• Familiarity with CI/CD tools such as GitHub Actions and ML model serving technologies such as MLflow
• Ability to collaborate well across teams
• A solid foundation in at least one of software engineering, data engineering, distributed systems, or applied machine learning, and readiness to grow into other areas as required
• Будет плюсом: Python

• A friendly, collaborative team focused on knowledge sharing and continuous learning
• Opportunity to work on MLOps and Machine Learning infrastructure using Java, Python, AWS, Docker, Kafka, Spark, and MLflow to power real-time ML systems