Description

You will build and maintain the infrastructure and pipelines required to operationalize machine learning models at scale.

Responsibilities

• Deploy data science models as batch processes using SageMaker pipelines, jobs, Step Functions, or EMR.
• Design, implement, and maintain scalable AWS SageMaker pipelines for model training, validation, deployment, and monitoring.
• Implement and manage MLflow tracking servers for experiment tracking and model registry.
• Automate ML workflows using MLflow, Airflow, and AWS Lambda.
• Build CI/CD pipelines for ML workflows using CodePipeline, GitHub Actions, or Azure DevOps.
• Integrate ML pipelines into enterprise data architecture, including data lakes and warehouses.

Required Skills

• 8+ years of professional experience.
• Hands-on experience with AWS SageMaker Pipelines, Lambda, and S3.
• Proficiency in setting up and managing MLflow servers.
• Strong Python and SQL programming skills.
• Experience with cloud-native and containerized services including Docker and Kubernetes.
• Solid understanding of classification models and supervised learning techniques.
• Knowledge of Enterprise Data Operations (EDO) practices, including data quality, lineage, and governance.
• Familiarity with metadata management tools such as Collibra, Informatica, or Alation.

Preferred Skills

• Experience deploying real-time models on SageMaker endpoints.
• Experience with AWS IAM, SNS, and CloudWatch.
• Experience with Snowflake databases and relational data.
• Experience in the Insurance domain.

Key Skills

Education

Any Gradute