Machine Learning Engineer

Insight Global; United States; On-site; USD 194,848,000 - 214,906,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

HSA/FSA/DCFSA accounts

401k with employer matching

Paid time off

Job summary

Insight Global seeks a Senior MLOps Engineer to internalize a vendor-provided ML solution and turn it into a scalable, production-grade platform. You will own the full ML lifecycle, working with stakeholders and vendor teams to understand model architecture, data pipelines, and deployment strategies.

This role emphasizes operationalizing, monitoring, documenting, and improving ML systems in a cloud-based environment, from proof-of-concept to production, with a highly visible initiative.

Qualifications

• 5+ years in ML engineering, MLOps, or related field.
• Hands-on production ML deployment experience.
• Experience with Databricks, MLflow, and Airflow.
• Proficiency with SQL and large datasets.
• Strong documentation and communication skills.

Responsibilities

• Review existing ML models, data sources, and architecture with external vendors.
• Plan transition from proof-of-concept to internal infrastructure.
• Deploy, monitor, and optimize ML models in production.
• Design scalable MLOps processes including versioning, monitoring, governance, retraining workflows.
• Build orchestration pipelines using Airflow or similar tools.
• Collaborate with Data Engineers and Stakeholders on end-to-end ML initiatives.
• Document architecture, deployment processes, and operational procedures.
• Establish monitoring to track model performance, drift, and health.
• Support enhancements and expansions of ML capabilities across the organization.

Skills

ML engineering

SQL

Documentation

Stakeholder collaboration

Tools

Databricks

MLflow

Airflow

XGBoost/LightGBM

Job description

Duration: Through End of 2026 with Strong Potential for Extension

Interview Process: One Round

Pay Rate: 68-75/hr

W2 (No sponsorship or c2c)

About the Opportunity

Seeking a Senior MLOps Engineer to help bring a machine learning solution in-house from a third-party vendor and transform it into a scalable, production-grade platform. This is a highly visible initiative where you'll own the full machine learning lifecycle, working directly with stakeholders and vendor teams to understand existing model architecture, training methodologies, data pipelines, and deployment strategies.

This role is ideal for someone who enjoys taking machine learning solutions from proof of concept to production and wants ownership of operationalizing, monitoring, documenting, and continuously improving ML systems in a cloud-based environment.

What You'll Do

• Partner with external vendors to review existing machine learning models, training processes, data sources, and architectural decisions.
• Assess current proof-of-concept solutions and develop strategies for transitioning them to internal infrastructure.
• Deploy, maintain, and optimize machine learning models in production environments.
• Design and implement scalable MLOps processes, including model versioning, monitoring, governance, and retraining workflows.
• Build and maintain orchestration pipelines using Airflow or similar workflow management tools.
• Work with large datasets to understand data collection, transformation, feature engineering, and model training methodologies.
• Collaborate with Data Engineers, Business Stakeholders, and Technical Teams to support end-to-end machine learning initiatives.
• Develop technical documentation covering architecture, deployment processes, model behavior, and operational procedures.
• Establish monitoring frameworks to track model performance, drift, reliability, and operational health.
• Support ongoing enhancements, integrations, and expansion of machine learning capabilities across the organization.

Required Qualifications

• 5+ years of experience in Machine Learning Engineering, MLOps, or a related field.
• Hands-on experience deploying and supporting machine learning models in production environments.
• Experience with Databricks and machine learning deployment workflows.
• Experience with MLflow, model registries, experiment tracking, and model lifecycle management.
• Experience deploying and supporting traditional machine learning models such as XGBoost, Random Forest, LightGBM, or similar frameworks.
• Experience building and managing orchestration workflows using Apache Airflow or similar tools.
• Strong SQL skills and experience working with large-scale datasets.
• Experience implementing monitoring, logging, model versioning, and retraining strategies.
• Ability to understand and evaluate data pipelines, feature engineering processes, and model training methodologies.
• Excellent documentation and communication skills.

Preferred Qualifications

• Experience with cloud platforms such as Azure, AWS, or GCP.
• Exposure to CI/CD pipelines for machine learning deployments.
• Experience supporting production AI and machine learning platforms at enterprise scale.
• Knowledge of model governance, model drift detection, and MLOps best practices.
• Experience transitioning machine learning solutions from proof of concept to production environments.
• Familiarity with Databricks Workflows, Feature Store, Unity Catalog, or related tools.

Exact compensation may vary based on several factors, including skills, experience, and education.

Benefit packages for this role will include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law