GCP AI/ML Engineer

GCP AI/ML Engineer We are seeking a talented and experienced GCP AI/ML Engineer to design, build, and operationalize scalable machine learning solutions on Google Cloud Platform (GCP). This role focuses on developing production-grade ML pipelines, automating workflows, and ensuring reliability and governance across enterprise AI platforms. The ideal candidate will have strong expertise in Vertex AI, MLOps, and cloud-native ML architectures, with a passion for turning data science models into scalable, production-ready systems. Key ResponsibilitiesML Pipeline Development & Automation Build, deploy, and manage production-grade machine learning pipelines using Vertex AI Pipelines and GCP-native services. Design automated workflows for data ingestion, feature engineering, model training, evaluation, and inference. Orchestrate ML workflows using Python, Vertex AI, BigQuery, and Cloud Storage. Ensure pipelines are modular, reusable, and scalable across use cases. Model Operationalization (MLOps) Operationalize the end-to-end ML lifecycle, including: Model training DeploymentMonitoringRetraining and lifecycle management Deploy models using Vertex AI endpoints with support for online and batch predictions. Implement robust CI/CD pipelines for ML artifacts and workflows. Enable automated model retraining and versioning strategies. Data Integration & Feature Engineering Enable seamless data flows across data lakes, warehouses, and ML platforms. Design and manage feature pipelines for training and inference datasets. Integrate with BigQuery, Cloud Storage, and streaming sources to support real-time and batch ML use cases. Ensure consistency between training and serving data pipelines. Model Monitoring & Performance Optimization Implement model monitoring solutions to track: Prediction accuracy Data drift and concept drift Model performance degradation Set up alerting mechanisms and dashboards for proactive issue detection. Optimize model performance and infrastructure for scalability, latency, and cost efficiency. AI Platform Engineering Build and enhance enterprise AI/ML platforms with a focus on: Automation ObservabilityReliabilityDevelop standardized frameworks for repeatable and governed ML deployments. Establish best practices for MLOps, pipeline orchestration, and infrastructure management. Collaboration & Cross-Functional Engagement Collaborate closely with: Data Scientists to productionize models Data Engineers for data pipeline integration Architects for scalable cloud designs Translate business requirements into deployable ML solutions. Provide technical leadership and mentoring on ML engineering practices. Governance, Security & Best Practices Implement model governance frameworks including auditability, lineage, and compliance. Ensure secure handling of data and models using IAM roles and access policies. Promote best practices in: Code versioning (Git)CI/CDTesting and validation Drive documentation and standardization across ML workflows.