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

Job Title: Machine Learning Engineer (GCP, Vertex AI, Dataproc, Apache Iceberg)
Location: Onsite in Charlotte, NC

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.