Description

You will own the end-to-end automation and deployment of machine learning models into production environments.

Responsibilities

• Design and maintain CI/CD pipelines for ML applications using AWS CodePipeline, CodeCommit, and CodeBuild.
• Automate model deployment and lifecycle management using Amazon SageMaker, Databricks, and MLflow.
• Develop and deploy AWS Lambda functions to trigger workflows and automate pre/post-processing.
• Orchestrate multi-stage ML pipelines including data ingestion and training using Airflow (MWAA) or Databricks Workflows.
• Manage infrastructure-as-code via CloudFormation or Terraform to ensure reproducible deployments.
• Implement model monitoring, drift detection, and alerting using CloudWatch, Prometheus, or Datadog.

Required Skills

• 5+ years of hands-on experience deploying ML applications in production at scale.
• Proficiency in AWS services: SageMaker, Lambda, CodePipeline, CodeCommit, ECR, ECS/Fargate, and CloudWatch.
• Strong experience with Databricks workflows, Databricks Model Serving, and MLflow.
• Advanced Python and shell scripting skills.
• Experience containerizing applications using Docker.
• Expertise in CI/CD principles, including testing ML pipelines and data validation.
• Experience orchestrating workflows with Airflow (MWAA) or Databricks Workflows.
• Knowledge of model monitoring stacks such as Prometheus, ELK, Datadog, or OpenTelemetry.
• Proficiency with Git/GitHub and automated deployment rollback strategies.

Preferred Skills

• Experience with Feature Stores, Kubeflow, SageMaker Pipelines, or Vertex AI.
• Exposure to LLM-based models, vector databases, or RAG pipelines.
• Knowledge of Terraform, AWS CDK, or A/B testing/shadow deployments.

Key Skills

Education

Any Graduate