Machine Learning Engineer

New York Technology Partners; Charlotte (NC); On-site; USD 120,000 - 180,000; Full time

Job summary

New York Technology Partners is hiring a Machine Learning Engineer to build and operationalize scalable ML solutions on Google Cloud Platform (GCP). You will deploy models from Data Scientists, run automated pipelines, and monitor production performance to ensure reliability and cost efficiency.

Responsibilities include designing data processing with Dataproc, PySpark, and Spark SQL, maintaining data quality, and collaborating across Data Engineers and DevOps to govern data lifecycles on Vertex

Qualifications

• Bachelor's degree or higher in CS, engineering, data science, or related field.
• 5+ years of data engineering or ML engineering experience.
• Strong experience with Google Cloud Platform (GCP).
• Proficiency in Python and SQL for data processing and ML workflows.
• Experience building distributed data processing and ML pipelines.
• Understanding of MLOps concepts, model lifecycle management, and deployment strategies.

Responsibilities

• Deploy and manage ML models from Data Scientists on Vertex AI.
• Design automated ML pipelines for batch and near real-time scoring.
• Configure Vertex AI training, model registry, and endpoints.
• Monitor model performance, latency, and reliability in production.
• Build scalable data processing with Dataproc, PySpark, and Spark SQL.
• Create robust data ingestion, transformation, and feature engineering pipelines.
• Optimize distributed workloads for performance and cost efficiency.
• Ensure data quality and governance across ML workflows.
• Develop large-scale data lakes using Apache Iceberg with partitioning and versioning.
• Collaborate with data platform teams to enforce enterprise standards.
• Provide technical leadership on cloud-native ML engineering best practices.

Skills

Python

SQL

GCP

ML Pipelines

MLOps

Education

Bachelor's degree in Computer Science, Engineering, Data Science, or related field

Tools

Vertex AI

Dataproc

BigQuery

GCS

Docker

Kubernetes

PySpark

Iceberg

Job description

Location: Onsite in Charlotte, NC

Job Title

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.
• 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.
• 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.
• 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.
• 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).
• 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

• Vertex AI
• Data Processing
• Apache Iceberg
• Programming
• MLOps

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.