ML Platform Engineer – Google Cloud (GCP) and Vertex AI

Requirements

• Expertise in cloud platforms, ML engineering, data pipelines and CI/CD for deploying and managing machine learning solutions.
• Google Cloud Platform (GCP) services: AI Platform (Vertex AI), Cloud Storage, BigQuery, Cloud Functions, Cloud PubSub, Cloud Build, Airflow, and Cloud Run.
• Understanding of ML concepts and LLMs (training, validation, hyperparameter tuning, evaluation).
• Experience with TensorFlow, Keras, PyTorch, and scikit-learn.
• Data preprocessing, ETL, and data pipelines using PySpark and Scala using serverless dataproc.
• CI/CD for ML (MLOps)
• Knowledge of CI/CD tools like Looper Pro and Jenkins.
• Model versioning, continuous training, and deployment using Vertex AI pipelines.
• Automation Scripting
• Strong programming skills in Python, Bash, and SQL.
• Automation of workflows and ML pipelines.
• DevOps Containerization
• Kubernetes (GKE) and Docker for containerization and orchestration.
• Good to have Helm charts and YAML for Kubernetes deployments.
• Monitoring Observability
• Cloud Monitoring, Cloud Logging, Prometheus and Grafana for monitoring and alerting.
• Model performance monitoring with Vertex AI Model Monitoring.
• Security Compliance
• Understanding of VPC, firewall rules, and service accounts.
• Data Science
• Must understand general data science methods and the development life cycle.
• An MLOps Engineer responsible for building, automating, and managing scalable machine learning pipelines and deployments on Google Cloud Platform.