Senior MLOps Engineer (AWS SageMaker)

Job Title: Senior MLOps Engineer (AWS SageMaker)

Location: 100% Remote (US) Duration: 5 Months Employment Type: W2 Only

<>About the Role

We are seeking a Senior MLOps Engineer to design, build, and maintain enterprise-scale machine learning infrastructure on AWS. This role requires hands-on expertise in SageMaker, Terraform, CI/CD automation, model deployment, monitoring, and platform engineering. The ideal candidate has experience operationalizing ML models from development through production while ensuring scalability, security, and reliability.

<>Required Skills

• 5+ years of experience in MLOps, ML Platform Engineering, or ML Infrastructure Engineering.
• Strong expertise with AWS SageMaker, including:
  • Training Jobs
  • SageMaker Pipelines
  • Model Registry
  • Real-Time Endpoints
  • Model Deployment & Monitoring
• Experience with AWS services:
  • S3
  • IAM
  • KMS
  • Lambda
  • Step Functions
  • CloudWatch
• Strong Infrastructure as Code experience using Terraform.
• Experience building CI/CD pipelines using GitLab CI, GitHub Actions, or similar tools.
• Hands-on experience with Docker and Git workflows.
• Strong Python development skills.
• Experience with model monitoring, drift detection, and production ML systems.
• Understanding of ML concepts including:
  • Feature Engineering
  • Model Evaluation
  • AUC
  • Calibration
  • C-Index
• Knowledge of security best practices including IAM, encryption, and secrets management.

<>Preferred Skills

• Experience supporting LLM workloads.
• AWS Bedrock experience.
• Experience with automated model promotion and rollback strategies.
• Healthcare or Life Sciences industry experience.
• Experience implementing AIOps, anomaly detection, and auto-remediation.

<>Responsibilities

• Build and maintain SageMaker-based ML platforms and deployment pipelines.
• Develop Terraform modules for AWS ML infrastructure.
• Design and manage CI/CD pipelines for ML lifecycle automation.
• Implement model versioning, monitoring, drift detection, and observability.
• Support production ML services and endpoint reliability.
• Collaborate with Data Scientists to deploy and operationalize machine learning models.
• Implement governance, security, and cost optimization across ML platforms.