Global is building a next-generation AI/ML platform on AWS and needs a Senior Machine Learning Engineer to bridge the gap between data science exploration and robust, production-ready ML systems, enabling them to scale smarter and faster.
Requirements
- 4+ years of experience in MLOps, DevOps, Data Engineering, or Software Engineering with a focus on machine learning operations and infrastructure.
Responsibilities
- Design, build, and maintain ML pipelines for training, evaluation, and inference using AWS SageMaker and related tools
- Implement and manage cloud infrastructure using AWS CDK or Terraform, and automate deployments via CI/CD workflows
- Collaborate with data scientists to productionize features and maintain high-performance pipelines using Python, SQL, pandas, and dbt
- Monitor deployed models for performance, drift, and data quality, with automated retraining or rollback as needed
- Ensure data integrity and system reliability through validation checks, logging, and alerting
- Troubleshoot and resolve production issues related to model serving, infrastructure, or data dependencies
- Contribute to architecture discussions and document infrastructure, workflows, and design decisions to support collaboration and scalability
Other
- Ability to work independently and take ownership of projects from design to deployment
- Strong written and verbal communication skills for cross-functional collaboration
- Comfortable working in a fast-paced environment with shifting priorities and deadlines
- Willingness to learn new tools, frameworks, and cloud services as needed
- Experience working with cross-functional teams including Data Science, Analytics, Engineering, and Product
