Lead Machine Learning Engineer

Revel IT; Columbus (OH); On-site; USD 120,000 - 195,000; Full time

Job summary

Revel IT is seeking a Lead Machine Learning Engineer to design, build, deploy, and operate production-grade ML platforms that empower data-driven decisions across Marketing, Finance, Product, and Customer Experience.

This role focuses on building scalable ML infrastructure, improving developer productivity, and ensuring ML systems run reliably in production while collaborating with data scientists and business teams to deliver high-impact models.

Qualifications

• 5+ years of experience designing, deploying, and supporting production machine learning systems.
• Strong Python programming and software engineering experience.
• Experience with production monitoring, logging, alerting, debugging, and incident response for ML systems.
• Experience deploying ML models using modern MLOps practices and tools.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Experience with Infrastructure as Code tools such as Terraform.
• Strong understanding of CI/CD, APIs, containerization, and production software development best practices.
• Excellent communication skills with the ability to collaborate effectively across technical and non-technical teams.

Responsibilities

• Design, build, deploy, and maintain scalable machine learning systems and production ML pipelines.
• Partner closely with Data Scientists to productionize machine learning models, forecasting solutions, and simulation workflows.
• Develop reusable tools, services, and infrastructure that accelerate the path from experimentation to production.
• Build and improve ML deployment, monitoring, versioning, and operational workflows to increase reliability and scalability.
• Monitor production ML systems, investigate incidents, troubleshoot issues, and implement long-term improvements.
• Design solutions that provide reproducibility, lineage, governance, and visibility across complex ML workflows.
• Integrate ML systems with upstream data sources, feature stores, cloud data platforms, APIs, and downstream business applications.
• Develop dashboards, monitoring, and reporting capabilities that help stakeholders evaluate model performance and business impact.
• Collaborate with engineering, data science, and business teams to deliver reliable machine learning solutions.
• Mentor engineers, share best practices, and contribute to architecture and engineering standards.

Skills

Python programming

Production ML systems

Cloud platforms

CI/CD

APIs

Containerization

Communication

Education

Bachelor's degree in CS/Engineering/Math/Stats

Master's or PhD in related field

Tools

Terraform

MLflow

Airflow

Kubeflow

Dagster

Databricks

Spark

Job description

Our client is seeking a Lead Machine Learning Engineer to help build and scale the machine learning systems that power critical business decisions across Marketing, Finance, Product, and Customer Experience. This is an opportunity to join a collaborative engineering organization where you\'ll design, deploy, and operate production-grade machine learning platforms that enable data scientists to deliver high-impact models reliably and efficiently.

This role is ideal for an experienced Machine Learning Engineer who enjoys building scalable ML infrastructure, improving developer productivity, and ensuring machine learning systems perform reliably in production.

Responsibilities

• Design, build, deploy, and maintain scalable machine learning systems and production ML pipelines.
• Partner closely with Data Scientists to productionize machine learning models, forecasting solutions, and simulation workflows.
• Develop reusable tools, services, and infrastructure that accelerate the path from experimentation to production.
• Build and improve ML deployment, monitoring, versioning, and operational workflows to increase reliability and scalability.
• Monitor production ML systems, investigate incidents, troubleshoot issues, and implement long-term improvements.
• Design solutions that provide reproducibility, lineage, governance, and visibility across complex ML workflows.
• Integrate ML systems with upstream data sources, feature stores, cloud data platforms, APIs, and downstream business applications.
• Develop dashboards, monitoring, and reporting capabilities that help stakeholders evaluate model performance and business impact.
• Collaborate with engineering, data science, and business teams to deliver reliable machine learning solutions.
• Mentor engineers, share best practices, and contribute to architecture and engineering standards.

Required Qualifications

• Bachelor\'s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field.
• 5+ years of experience designing, deploying, and supporting production machine learning systems.
• Strong Python programming and software engineering experience.
• Hands-on experience building and maintaining machine learning pipelines from development through deployment and production support.
• Experience with production monitoring, logging, alerting, debugging, and incident response for ML systems.
• Experience deploying ML models using modern MLOps practices and tools.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Experience with Infrastructure as Code tools such as Terraform.
• Strong understanding of CI/CD, APIs, containerization, and production software development best practices.
• Excellent communication skills with the ability to collaborate effectively across technical and non-technical teams.

Preferred Qualifications

• Master\'s or Ph.D. in Computer Science, Engineering, Statistics, Mathematics, or a related field.
• Experience with ML platforms and orchestration tools such as MLflow, Airflow, Kubeflow, Dagster, Databricks, Snowflake, Spark, or dbt.
• Experience building shared ML platforms, developer tooling, or reusable infrastructure for machine learning teams.
• Experience with customer lifetime value modeling, forecasting, recommendation systems, fraud detection, or risk modeling.
• Background working in insurance, financial services, or another highly regulated industry.
• Experience supporting large-scale, cloud-based production ML environments.

What We're Looking For

We\'re looking for an engineer who has owned machine learning systems throughout their lifecycle—not just developed models, but deployed, monitored, maintained, and continuously improved them in production. The ideal candidate enjoys solving complex engineering challenges, building scalable platforms, and partnering closely with data scientists and business stakeholders to deliver reliable machine learning solutions.