We are seeking an experienced Senior Data Engineer (AI/ML) to lead the integration, deployment, and operationalization of machine learning models in a production-grade cloud environment. The ideal candidate brings deep technical expertise in Google Cloud Platform (GCP) with hands-on experience using Vertex AI, MLOps best practices, and infrastructure-as-code to deliver scalable, secure, and highly available ML solutions. Day-to-Day Responsibilities • Design, build, and optimize scalable ML inference pipelines using Vertex AI Pipelines, Model Endpoints, Model Monitoring, and Feature Store. • Collaborate cross-functionally with data scientists, software engineers, and DevOps to deploy and monitor ML models in production. • Automate the ML lifecycle, including CI/CD for ML, model retraining, versioning, shadow testing, and rollback strategies. • Implement model observability with tools such as Stackdriver, Prometheus, or Grafana. • Ensure seamless data ingestion, transformation, and curation pipelines from diverse data sources (DBMS, APIs, streaming, file systems). • Translate complex business needs into scalable data and ML solutions, aligning with architectural best practices. • Champion infrastructure automation via Terraform, Docker, and GCP Deployment Manager. Qualifications • Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience). • 5+ years in data engineering, data warehousing, or software engineering. • 4+ years of experience implementing full software development lifecycle (SDLC) practices in Agile environments. MUST HAVE Skills (Non-Negotiable) • 2+ years of hands-on experience with GCP Vertex AI, including Vertex Pipelines, Model Endpoints, Monitoring, and Feature Store. • Proven track record scaling ML models in production (real-time + batch), optimizing performance, cost, and throughput. • Strong knowledge of MLOps, CI/CD for ML, and deployment in containerized environments. Preferred Skills • Familiarity with Kubeflow, MLFlow, or TensorFlow Extended (TFX) for advanced MLOps. • Experience with hybrid/federated model deployments. • Skilled in monitoring/alerting frameworks (e.g., Stackdriver, Prometheus, Grafana). • Proficiency in BigQuery, Dataflow, Cloud Functions, and Python for data/ML integration and automation.