Overview

Responsibilities: • Deploy, monitor, and manage ML/AI models on Vertex AI, including training pipelines, model endpoints, and batch prediction workflows.

Responsibilities

• Deploy, monitor, and manage ML/AI models on Vertex AI, including training pipelines, model endpoints, and batch prediction workflows.
• Provide MLOps support, including troubleshooting model performance, debugging ML pipelines, and resolving production incidents.
• Implement AIOps practices, including monitoring, alerting, and automated remediation for AI/ML systems using GCP tools.
• Collaborate with Data Scientists to operationalise ML models, optimise performance, and ensure reliability.
• Manage CI/CD pipelines for ML workflows using tools like Cloud Build, GitHub Actions, or Jenkins.
• Monitor and optimise GCP resources such as Big Query, Cloud Storage, Dataflow, Dataproc, Cloud Functions, Cloud Run, and Pub/Sub for AI/ML workloads.
• Ensure compliance with security, governance, and data privacy best practices in ML workflows.
• Build dashboards and observability solutions using Google Cloud Monitoring/Logging, Data Plex, and third-party tools.
• Troubleshoot and optimise ML pipelines, including data preprocessing, feature engineering, and model retraining.

Qualifications

• 2-4 years of experience in MLOps, AI operations, or ML engineering on cloud platforms, preferably GCP.
• Hands-on experience with Vertex AI for model deployment, training pipelines, and endpoint management.
• 2+ years of experience in code management and reliability using GitHub, pytest, and SonarQube.
• 1+ years of experience with Tableau.
• Strong knowledge of MLOps practices, including CI/CD, versioning, automated testing, and model monitoring.
• Proficiency in Python, SQL, and ML frameworks like TensorFlow or PyTorch.
• Familiarity with AI monitoring, alerting, and observability tools (Cloud Monitoring, Logging, Data plex).
• Strong problem-solving skills and ability to troubleshoot ML pipelines and AI system issues.
• Understanding of AIOps concepts, including automated anomaly detection, model drift detection, and incident response for AI systems.
• Familiarity with Kubeflow, Airflow, or orchestration tools for ML pipelines.

Nice to have

• GCP certifications, such as Professional Machine Learning Engineer or Professional Data Engineer.