Machine Learning Engineer (GCP, Vertex AI, Dataproc, Apache Iceberg)

Machine Learning Engineer (GCP, Vertex AI, Dataproc, Apache Iceberg) Job Summary We are seeking a highly skilled Machine Learning Engineer to build, deploy, and manage scalable machine learning solutions on Google Cloud Platform (GCP). The successful candidate will be responsible for operationalizing machine learning models developed by Data Scientists, ensuring reliable execution, monitoring, performance optimization, and integration with enterprise data platforms. This role will focus on leveraging Vertex AI, Dataproc, Apache Spark, and Apache Iceberg to create production-grade ML pipelines capable of processing large-scale data and supporting advanced analytics and AI use cases. ________________________________________ Key Responsibilities Machine Learning Platform Engineering Deploy, execute, and manage machine learning models provided by Data Scientists using Vertex AI. Design and maintain automated ML pipelines for batch and near real-time scoring. Configure and manage Vertex AI training, model registry, endpoints, and prediction services. Monitor model execution, performance, latency, and operational health. Data Engineering & Processing Develop scalable data processing frameworks using Dataproc, PySpark, and Spark SQL. Build robust data ingestion, transformation, and feature engineering pipelines. Optimize distributed processing workloads for performance and cost efficiency. Ensure data quality, completeness, and consistency across ML workflows. Apache Iceberg Data Management Design and manage large-scale data lakes using Apache Iceberg. Implement partitioning, schema evolution, versioning, and time-travel capabilities. Optimize Iceberg table performance for machine learning and analytical workloads. Collaborate with data platform teams to establish enterprise data management standards. MLOps & Automation Implement

CI/CD

pipelines for ML deployment and model lifecycle management. Automate model retraining, scoring, validation, and monitoring workflows. Build observability frameworks including logging, alerting, metric collection, and operational dashboards. Establish governance controls for model execution and data lineage. Cloud Platform Management Manage GCP infrastructure supporting machine learning workloads. Optimize compute utilization across Vertex AI, Dataproc, BigQuery, GCS, and related services. Implement security, access controls, and cloud operational best practices. Support production incident resolution and platform reliability initiatives. Collaboration Partner with Data Scientists to operationalize new ML models. Work closely with Data Engineers, Architects, and DevOps teams. Translate business requirements into scalable AI/ML solutions. Provide technical leadership on cloud-native ML engineering best practices. ________________________________________ Required Qualifications Bachelor's degree in Computer Science, Engineering, Data Science, or related field. 5+ years of experience in Data Engineering, Machine Learning Engineering, or related roles. Strong experience with Google Cloud Platform (GCP). Hands-on expertise with Vertex AI Dataproc Apache Spark / PySpark Apache Iceberg BigQuery Cloud Storage (GCS) Strong proficiency in Python and SQL. Experience building distributed data processing and ML pipelines. Understanding of MLOps concepts, model lifecycle management, and deployment strategies. Familiarity with CI/CD tools and Infrastructure as Code. ________________________________________ Preferred Qualifications Experience with Kubeflow Pipelines or Vertex AI Pipelines. Knowledge of feature stores and model monitoring frameworks. Experience with Docker and Kubernetes. Familiarity with data governance, metadata management, and data lineage tools. Experience in financial services, AML, risk analytics, or large-scale regulated environments. ________________________________________ Technical Skills Cloud & Data Platforms Google Cloud Platform (GCP) Vertex AI Dataproc BigQuery Cloud Storage Data Processing Apache Spark PySpark Spark SQL Apache Iceberg Programming Python SQL Shell Scripting MLOps CI/CD Model Monitoring Pipeline Automation GitHub DevOps Practices ________________________________________ Success Metrics Reliable model deployment and execution in production. Reduced model operationalization time. Efficient and scalable ML pipelines. Improved platform reliability and monitoring. Optimized cloud resource utilization and cost management. High data quality and governance compliance. Ideal Candidate Profile A strong platform-oriented Machine Learning Engineer who can bridge Data Science and Data Engineering teams by transforming analytical models into scalable, governed, and production-ready AI solutions on GCP. #J-18808-Ljbffr TechDigital Group