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

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

Job Title
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.
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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.
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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:
o Vertex AI
o Dataproc
o Apache Spark / PySpark
o Apache Iceberg
o BigQuery
o 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.
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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.
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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
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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.