MLOps Engineer — AWS SageMaker
Client : A large global enterprise (name not disclosed)
Location : India
Work Model : 100% Remote
Contract : 6 months (initial) with possibility of extension
Start Date : ASAP
Engagement : Full-time / Long-term contract
Role Overview
You will work within a global data & analytics team to design, deploy, and maintain robust ML pipelines using AWS SageMaker and associated cloud services. The role requires strong experience in production-grade MLOps, automation, and cloud engineering.
Key Responsibilities
- Build, deploy, and maintain ML models using AWS SageMaker (Pipelines, Endpoints, Model Registry)
- Develop automated CI / CD workflows using CodePipeline, CodeBuild , or GitHub Actions
- Implement model monitoring, logging, and drift detection (CloudWatch, SageMaker Model Monitor)
- Create and maintain infrastructure using Terraform or CloudFormation
- Manage secure, scalable, cost-optimized AWS environments (IAM, VPC, networking)
- Collaborate with data scientists, cloud engineering teams, and solution architects
- Troubleshoot issues in high-availability, production ML setups
Required Experience
4–8 years total experience in MLOps / ML EngineeringHands-on experience with SageMaker in enterprise-scale environmentsStrong Python skills & familiarity with ML frameworksExperience with Docker, Kubernetes (EKS preferred)Experience building CI / CD pipelinesDeep practical knowledge of AWS ecosystemNice to Have
Experience implementing model governanceExperience with multi-model endpointsFamiliarity with enterprise security standards and compliance