Machine Learning Engineer

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: 68 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 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. Lets Connect Want to discuss this opportunity in more detail Feel free to reach out. Recruiter: Aswin Dhanvandhar Phone:; Extn :141 E-mail: LinkedIn: