Senior MLOps Engineer: End-to-End Production ML

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

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.