Machine Learning Engineer

• Develop, implement, and maintain machine learning models for pricing ancillary products such as seats, bags, extra legroom, and paid fare upgrades.
• Design, research, and implement end-to-end machine learning pipelines covering model training, retraining, deployment, and monitoring.
• Lead the MLOps aspects within the team, ensuring robust, scalable, and production-ready machine learning solutions.
• Design and optimize ML architectures to support reliable and efficient model development and deployment.
• Continuously monitor, maintain, and improve productionized machine learning models.
• Ensure low-latency model deployments and adherence to internal engineering standards and best practices.
• Work extensively within the Google Cloud Platform (GCP) ecosystem for machine learning development and deployment.
• Leverage BigQuery and the Vertex AI suite for data processing, model development, deployment, and monitoring.
• Implement infrastructure-as-code using Terraform to provision and manage ML infrastructure.
• Containerize machine learning applications and services using Docker.
• Build and maintain CI/CD pipelines using GitHub Actions.
• Implement testing, automation, and deployment practices to ensure reliable and scalable ML solutions.
• Collaborate with data science, engineering, and other technical stakeholders throughout the machine learning lifecycle., * Design and implement end-to-end production-grade machine learning pipelines.
• Develop and maintain ML models that address real-world pricing and product optimization problems.
• Manage the complete model lifecycle from development and retraining through deployment, monitoring, and continuous improvement.
• Design scalable ML architectures and optimize them for performance, reliability, and low-latency execution.
• Lead MLOps practices within a technical team and establish effective engineering standards.
• Build and maintain reliable CI/CD pipelines for machine learning applications.
• Automate infrastructure provisioning and management using Terraform.
• Containerize and deploy ML workloads using Docker.
• Work effectively with GCP, BigQuery, and Vertex AI for production machine learning solutions.
• Implement appropriate testing, monitoring, and deployment practices for production ML systems.
• Troubleshoot production ML and infrastructure issues and implement sustainable improvements.
• Collaborate effectively with data scientists, engineers, and other stakeholders.
• Apply software engineering and MLOps best practices to machine learning development.

What we bring to the table:

• The opportunity to work on production-grade machine learning and MLOps solutions.
• Exposure to real-world ML applications involving pricing and optimization of ancillary products.
• Opportunities to work extensively with GCP, BigQuery, and Vertex AI.
• Hands-on exposure to modern MLOps technologies including Terraform, Docker, and GitHub Actions.
• Opportunities to work across the complete machine learning lifecycle, from model development and retraining to deployment, monitoring, and optimization.
• A collaborative engineering environment focused on scalable, reliable, and high-performance machine learning solutions.
• Opportunities to contribute to ML architecture, automation, testing, CI/CD, and continuous improvement.

• 6-8 years of overall professional experience in Machine Learning, Data Science, or a closely related engineering discipline.
• Strong hands-on experience developing, implementing, and maintaining machine learning models in production environments.
• Strong understanding of the complete ML lifecycle, including model development, retraining, deployment, monitoring, and optimization.
• Strong MLOps experience with ownership of production machine learning workflows and infrastructure.
• Hands-on experience with Google Cloud Platform (GCP).
• Experience with BigQuery and the Vertex AI ecosystem.
• Strong experience with Terraform and infrastructure-as-code practices.
• Hands-on experience with Docker and containerized ML workloads.
• Strong experience building and managing CI/CD pipelines using GitHub Actions.
• Experience with ML architecture design, optimization, testing, and automation.
• Understanding of production ML monitoring, model performance, reliability, and low-latency deployment requirements.
• Strong understanding of scalable and maintainable machine learning engineering practices.