W2 ONLY | ONSITE ROLE
Position Title: Machine Learning Engineer (GCP, Vertex AI, Dataproc, Apache Iceberg)
Location: Charlotte, NC
Duration: 12+ Months Contract
Position Type- W2 Only
Exp Level- 10+Years
Req Skills- Machine Learning, Vertex AI, Dataproc, Apache Spark, PySpark, Apache Iceberg, GCP, BigQuery, Cloud Storage
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 IcebergBigQuery
- 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
- PythonSQL
- Shell Scripting
- MLOps
- CI/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.