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Role: Machine Learning Engineer- 2 Positions Overall experience of minimum 7 years and machine learning experience of at least 3 - 4 years. Location- Remote Overview: As a GCP ML Engineer, you'll design, develop, and maintain machine learning pipelines and infrastructure on the Google Cloud Platform (GCP). You'll work closely with data scientists, engineers, and DevOps teams to ensure smooth integration and deployment of machine learning models. Key Responsibilities: Pipeline Development: Build and automate end-to-end machine learning pipelines from data ingestion to model deployment. Infrastructure Management: Develop and manage infrastructure for scalable machine learning solutions using GCP services such as AI Platform, Cloud Functions, BigQuery, and Kubernetes. CI/CD for ML Models: Implement CI/CD processes for machine learning models, ensuring reliable and scalable deployment practices. Monitoring & Optimization: Monitor and optimize machine learning models in production, ensuring high performance and uptime. Collaboration: Work with cross-functional teams, including data engineers, software developers, and product teams, to ensure the successful deployment and operation of models. Technical Requirements: Experience with Google Cloud Platform (GCP), including GKE, AI Platform, Dataflow, and BigQuery services. Proficiency in Python and frameworks like TensorFlow, PyTorch, or Scikit-learn. Knowledge of Kubernetes and containerization (Docker). Experience with CI/CD tools such as Jenkins, CircleCI, or GitLab for ML pipelines. Strong knowledge of DevOps principles and tools (Terraform, Ansible). Preferred Qualifications: Hands-on experience with MLFlow or Kubeflow. Familiarity with data engineering processes, ETL pipelines, and data lakes. Originally posted on Himalayas