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.
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