Design, deploy, and maintain robust ML pipelines using AWS SageMaker. Collaborate with data scientists and cloud engineering teams. Develop automated CI/CD workflows and manage secure, scalable AWS environments.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
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 Engineering
- Hands-on experience with SageMaker in enterprise-scale environments
- Strong Python skills & familiarity with ML frameworks
- Experience with Docker, Kubernetes (EKS preferred)
- Experience building CI/CD pipelines
- Deep practical knowledge of AWS ecosystem
Nice to Have
- Experience implementing model governance
- Experience with multi-model endpoints
- Familiarity with enterprise security standards and compliance
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