Description

You will build and maintain scalable MLOps pipelines using AWS SageMaker to support the full ML lifecycle.

This role is remote.

Responsibilities

• Build and maintain scalable MLOps pipelines using AWS SageMaker for ingestion, training, versioning, and deployment.
• Optimize models for real-time inference via APIs and detect data or model drift.
• Automate re-training workflows and use feature stores and model registries effectively.
• Collaborate with data science, ML, and engineering teams to architect end-to-end AI/ML solutions.
• Build and maintain CI/CD pipelines for ML using Git or CodePipeline.

Required Skills

• 8+ years of experience in MLOps Engineering.
• Strong experience with Amazon SageMaker, covering model training, deployment, and monitoring.
• Proficiency in ML libraries including TensorFlow and PyTorch.
• Solid experience with streaming platforms like Apache Kafka or Spark Streaming.
• Experience with containerization using Docker and orchestration with Kubernetes/EKS.
• Familiarity with AWS services: S3, Lambda, CloudWatch, Step Functions, and Glue.
• Experience building and maintaining CI/CD pipelines for ML using Git or CodePipeline.

Key Skills

Education

Any Gradute