Description

You will manage the end-to-end lifecycle of machine learning models within an enterprise data operations framework.

Responsibilities

• Deploy models built by data scientists as batch models using SageMaker pipelines, jobs, Step Functions, or EMR.
• Design, implement, and maintain scalable SageMaker pipelines for training, validation, deployment, and monitoring.
• Automate ML workflows using MLflow, Airflow, and AWS Lambda.
• Set up and manage MLflow tracking servers for experiment tracking and model registry.
• Build and optimize classification models using large-scale datasets in Amazon S3.
• Ensure robust 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

• 5+ years of experience in ML or Data Engineering roles.
• Hands-on experience with AWS SageMaker Pipelines, Lambda, and S3.
• Proficiency in setting up and managing MLflow servers.
• Strong Python and SQL programming skills.
• Solid understanding of classification models and supervised learning techniques.
• Experience implementing data pipelines using Docker and Kubernetes.
• Familiarity with data governance, lineage, and metadata management using Collibra, Informatica, or Alation.
• Strong knowledge of Enterprise Data Operations (EDO) practices.
• Bachelor of Computer Science degree.

Preferred Skills

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

Key Skills

Education

BACHELOR OF COMPUTER SCIENCE