Note: The job is a remote job and is open to candidates in USA. Revel IT is seeking a Lead Machine Learning Engineer to help build and scale the machine learning systems that power critical business decisions across various domains. The role involves designing, deploying, and operating production-grade machine learning platforms while collaborating with data scientists and engineering 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
- 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
- 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
Benefits
- Fully remote within the United States
- Competitive bonus opportunity
- Equity offering
- Comprehensive benefits package
Company Overview