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.