Overview

Senior MLOps Engineer needed to build and scale machine learning infrastructure on AWS, integrating ML engineering, cloud infrastructure, and developer productivity.

Responsibilities

• Design, build, and operate ML/AI development platforms on AWS using services such as SageMaker (Studio, Training, Real-Time & Async Inference, Pipelines, Feature Store), S3, Glue, Lambda, ECS/EKS.
• Implement infrastructure-as-code using Terraform or equivalent, manage workflow orchestration using AWS Step Functions or Airflow.
• Build automated data ingestion and transformation pipelines using S3, Glue, EMR/Spark, Redshift, incorporating data quality and lineage tooling like Great Expectations, Deequ.
• Develop CI/CD pipelines for ML with Code Build, Code Pipeline, or GitHub Actions, integrating unit tests, data contract checks, model validation, canary/shadow deployments, and automated rollback strategies.
• Deploy real-time inference endpoints (SageMaker endpoints or Fast API-based services on Lambda/ECS/EKS) and scalable batch processing jobs.
• Define SLOs and implement observability practices.

Qualifications

• Senior MLOps Engineer experience with ML platform design and operation.
• AWS expertise, including SageMaker, S3, Glue, Lambda, ECS/EKS.
• Experience with infrastructure-as-code tools (Terraform).
• Knowledge of workflow orchestration using AWS Step Functions or Airflow.
• CI/CD pipeline development experience for ML workflows.
• Model deployment and scaling practices.

Nice to have

• Familiarity with observability tools.
• Experience with great expectations or Deequ for data quality enforcement.