Key Skills

Job Description

Position : Senior Machine Learning Engineer – MLOps / GCP Type : Contract Duration : 6 to 12 Months Develop, implement, and maintain machine learning models for pricing ancillary products like seats, bags, legroom, and paid fare upgrades.Design, research, and implement the entire ML pipeline, from retraining to model deployment and monitoring. Continuously monitor and improve the productionized ML models, ensuring low-latency deployments and adherence to internal standards and best practices. Work within the Google Cloud Platform (GCP) ecosystem, leveraging tools such as BigQuery and the VertexAI suite for model development and deployment.Utilize Terraform for infrastructure-as-code, Docker for containerization, and Github Actions for CI/CD pipelines, ensuring robust and scalable solutions. Lead the MLOps aspect within the team. (e.g. expertise in Terraform, CI/CD pipelines, testing, ML architecture design and optimisation.) Develop, implement, and maintain machine learning models for pricing ancillary products like seats, bags, legroom, and paid fare upgrades. Design, research, and implement the entire ML pipeline, from retraining to model deployment and monitoring. Continuously monitor and improve the productionized ML models, ensuring low-latency deployments and adherence to internal standards and best practices. Work within the Google Cloud Platform (GCP) ecosystem, leveraging tools such as BigQuery and the VertexAI suite for model development and deployment.Utilize Terraform for infrastructure-as-code, Docker for containerization, and Github Actions for CI/CD pipelines, ensuring robust and scalable solutions

Core Responsibilities

Develop and maintain machine learning models for pricing ancillary products and design the end-to-end ML pipeline from retraining to deployment. Lead the MLOps strategy by optimizing infrastructure and ensuring low-latency production deployments.

Requirements

Requires expertise in the GCP ecosystem, specifically Vertex AI and BigQuery, along with proficiency in Terraform and Docker. Candidates must have strong experience in CI/CD pipelines and ML architecture optimization.