Job description

Machine Learning Engineer – GCP / Vertex AI / Dataproc / Apache Iceberg

Location: Charlotte, NC.

No OPT/CPT

Key Responsibilities

Deploy and manage ML models using Google Vertex AI.
Build automated ML pipelines for batch and near real-time scoring.
Develop scalable data processing pipelines using Dataproc, Apache Spark, PySpark, and Spark SQL.
Design and optimize large-scale data lakes using Apache Iceberg.
Implement partitioning, schema evolution, versioning, and time-travel capabilities.
Build data ingestion, transformation, and feature engineering workflows.
Implement MLOps, CI/CD, model monitoring, retraining, and automation.
Work with BigQuery and Google Cloud Storage (GCS).
Monitor model performance, pipeline health, logging, metrics, and alerts.
Optimize GCP compute resources and cloud costs.
Support production incidents, reliability, security, and governance.

Required Skills

✅ 7+ years of experience in Machine Learning Engineering, Data Engineering, or related areas.

✅ Strong GCP experience.

✅ Hands-on Vertex AI experience.

✅ Dataproc.

✅ Apache Spark / PySpark / Spark SQL.

✅ Apache Iceberg.

✅ Python and SQL.

✅ BigQuery and GCS.

✅ Experience building distributed data and ML pipelines.

✅ Strong understanding of MLOps and ML model lifecycle management.

✅ CI/CD and DevOps experience.

Preferred Skills.

Vertex AI Pipelines / Kubeflow Pipelines.
Docker / Kubernetes.
Feature Stores.
Model Monitoring.
Terraform / Infrastructure as Code.
Data Governance / Metadata / Data Lineage.
Financial Services, AML, Fraud, Risk Analytics, or regulated environments.

Ideal Candidate

We are looking for a platform-oriented Machine Learning Engineer who can bridge the gap between Data Science and Data Engineering and transform ML models into scalable, governed, production-ready solutions on GCP.

If you have strong experience with GCP + Vertex AI + Dataproc/PySpark + Apache Iceberg + MLOps, we'd love to connect!