Whom we are looking for

<p>💼 Key Responsibilities:<br> Build automated ML pipelines for training, validation, deployment, and monitoring using Vertex AI Pipelines, Kubeflow, or TFX<br> Leverage Vertex AI Workbench, Training, and Experiments for reproducible, collaborative model development<br> Deploy, manage, and monitor models using Vertex AI Model Registry, Model Monitoring, and Prediction Endpoints<br> Collaborate with Data Scientists to productionize notebooks and prototypes into scalable ML services<br> Monitor model performance, detect data/model drift, and ensure data quality using Vertex AI Monitoring<br> Containerize and orchestrate ML workloads using Docker and Kubernetes (GKE)<br> Build and maintain robust CI/CD pipelines using Cloud Build, Jenkins, or Bitbucket Pipelines<br> Ensure strong version control, security, and compliance across the ML lifecycle<br> Maintain comprehensive documentation, templates, and artifacts to enable reproducibility, governance, and fast onboarding<br></p> <p>🛠 Skills & Tools:<br> Vertex AI (Pipelines, Training, Workbench, Model Registry, Monitoring, Prediction Endpoints)<br> GCP Core Services: GCS, BigQuery, Pub/Sub, Dataflow<br> MLOps Tools: Kubeflow, TFX, MLflow (optional)<br> DevOps Tools: Docker, Kubernetes (GKE), CI/CD (Cloud Build, Jenkins)<br> Languages: Python, YAML, Shell scripting<br> Version Control & Tracking: Git, DVC (optional), TensorBoard<br></p> <p>✅ Preferred Qualifications:<br> GCP Professional Machine Learning Engineer certification is a plus<br> Experience working with regulated environments and secure ML pipelines<br> Strong understanding of ML lifecycle, MLOps principles, and cloud cost<br></p>