ML Engineer

The Intersect Group; Atlanta (GA); Hybrid; USD 120,000 - 155,000; Full time

Job summary

The Intersect Group is actively seeking a Machine Learning Engineer to join a growing MLOps team focused on building, deploying, and supporting production-grade machine learning solutions at scale. You will partner with Data Scientists, Data Engineers, and business stakeholders to productionize models and deliver measurable business value.

In this fully remote contract-to-hire role, you will design scalable pipelines, implement monitoring, and continuously improve ML systems across multiple

Qualifications

• 3+ years of experience building, deploying, and supporting production machine learning solutions.
• Strong hands-on experience implementing MLOps principles and best practices.
• Proficiency with Google Cloud Platform, Vertex AI, Python, and data tooling.

Responsibilities

• Design, develop, and maintain end-to-end ML pipelines for production environments.
• Deploy, monitor, and optimize ML models for scalability, reliability, and performance.
• Build automated workflows supporting model training, validation, deployment, and lifecycle management.
• Track model performance, drift, and operational metrics with proactive monitoring.

Skills

MLOps

Python

Vertex AI

BigQuery

Cloud Monitoring

Model monitoring

Tools

Google Cloud Platform

Vertex AI

BigQuery

Dataform

Job description

## ML Engineer**Atlanta,GA30339**Posted: 08/28/2026Employment Type:Contract to HireCategory: IT - AI/MLJob Number: 250989Work Location: Fully Remote## Job Description**Role Summary** We are seeking a Machine Learning Engineer to join a growing MLOps team focused on building, deploying, and supporting production grade machine learning solutions at scale. This role offers the opportunity to work across a diverse portfolio of initiatives while helping drive the adoption and operationalization of machine learning throughout the organization. In this role, you will partner closely with Data Scientists, Data Engineers, and business stakeholders to bring machine learning models into production and ensure they deliver measurable business value. You will design scalable ML pipelines, implement monitoring and observability practices, and continuously improve the reliability and performance of machine learning systems across multiple business domains. **Key Responsibilities** • Design, develop, and maintain end to end machine learning pipelines for production environments • Deploy, monitor, and optimize machine learning models to ensure scalability, reliability, and performance • Build automated workflows supporting model training, validation, deployment, and lifecycle management • Track model performance, drift, and operational metrics while implementing proactive monitoring solutions • Collaborate with Data Scientists and Data Engineers to productionize machine learning solutions • Implement observability and monitoring practices for machine learning platforms and services • Support multiple machine learning initiatives across various business functions and use cases • Troubleshoot system issues and continuously improve the efficiency, scalability, and stability of ML infrastructure **Key Requirements** • 3+ years of experience building, deploying, and supporting production machine learning solutions • Strong hands on experience implementing MLOps principles, methodologies, and best practices • Proven expertise with Google Cloud Platform and cloud based machine learning environments • Strong experience with Vertex AI for model development, deployment, orchestration, and monitoring • Proficiency with Python, BigQuery, Cloud Monitoring, and modern machine learning development tools • Experience building and managing automated machine learning pipelines in enterprise environments • Knowledge of model monitoring, performance optimization, observability, and machine learning lifecycle management • Strong communication, collaboration, analytical thinking, and problem solving skills **Preferred Qualifications** • Experience with Dataform and data transformation workflows • Exposure to data engineering concepts, data pipelines, and large scale data processing environments • Experience supporting enterprise scale machine learning platforms and initiatives • Familiarity with supply chain, fulfillment, logistics, delivery, or operational analytics use cases • Experience working within Agile development environments and cross functional teams • Knowledge of cloud architecture, automation, and platform engineering best practices **What You'll Work On** • Smart Fulfillment initiatives • Supply Chain optimization programs • Express Delivery solutions • Enterprise machine learning projects across multiple business functions • Modern MLOps and cloud based machine learning platforms **Why Join This Opportunity** • Work on diverse machine learning initiatives with significant business impact • Gain exposure to modern MLOps practices and enterprise scale AI solutions • Collaborate with experienced Data Science, Data Engineering, and Technology teams • Expand your expertise across multiple business domains and machine learning use cases • Contribute to the growth and evolution of a strategic enterprise machine learning program