Machine Learning Engineer

• Job Title : Machine Learning Engineer (GCP, Vertex AI, Dataproc, Apache Iceberg)
• Employment Type : Contract
• Location: Charlotte, NC (Onsite)

Job Description :

• 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.

• 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.