About the job

Help design, build and continuously improve the clients online platform.
Research, suggest and implement new technology solutions following best practices/standards.
Take responsibility for the resiliency and availability of different products.
Be a productive member of the team.

Requirements

Develop, implement, and maintain machine learning models for pricing ancillary products such as seats, baggage, extra legroom, and paid fare upgrades.
Design and manage the end-to-end ML lifecycle, including model development, retraining, deployment, monitoring, and optimization.
Continuously monitor and improve production ML models, ensuring low-latency performance, reliability, scalability, and compliance with engineering standards.
Build and deploy ML solutions within the Google Cloud Platform (GCP) ecosystem, leveraging BigQuery and Vertex AI.
Implement Infrastructure as Code (IaC) using Terraform and containerize ML applications with Docker.
Develop and maintain CI/CD pipelines using GitHub Actions to enable reliable and automated ML deployments.
Strong experience leading MLOps activities within an ML engineering team.
Expertise in Terraform, CI/CD, automated testing, and ML architecture design and optimization.
Hands-on experience with GCP, BigQuery, and Vertex AI.
Strong understanding of ML model deployment, monitoring, retraining, and production optimization.
Experience with Docker and GitHub Actions.

Responsibilities

Help design, build and continuously improve the clients online platform.
Research, suggest and implement new technology solutions following best practices/standards.
Take responsibility for the resiliency and availability of different products.
Be a productive member of the team.

Requirements

Develop, implement, and maintain machine learning models for pricing ancillary products such as seats, baggage, extra legroom, and paid fare upgrades.
Design and manage the end-to-end ML lifecycle, including model development, retraining, deployment, monitoring, and optimization.
Continuously monitor and improve production ML models, ensuring low-latency performance, reliability, scalability, and compliance with engineering standards.
Build and deploy ML solutions within the Google Cloud Platform (GCP) ecosystem, leveraging BigQuery and Vertex AI.
Implement Infrastructure as Code (IaC) using Terraform and containerize ML applications with Docker.
Develop and maintain CI/CD pipelines using GitHub Actions to enable reliable and automated ML deployments.
Strong experience leading MLOps activities within an ML engineering team.
Expertise in Terraform, CI/CD, automated testing, and ML architecture design and optimization.
Hands-on experience with GCP, BigQuery, and Vertex AI.
Strong understanding of ML model deployment, monitoring, retraining, and production optimization.
Experience with Docker and GitHub Actions.