Machine Learning Engineer (ID: 4050)

As a Machine Learning Engineer – MLOps, you will: 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. What You Bring to the Table: 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. You should possess the ability to: 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…