Description

Strong hands‑on experience with AWS SageMaker for end‑to‑end ML development (data prep, training, tuning, deployment, monitoring) Advanced proficiency in Python and ML frameworks (TensorFlow / PyTorch / Scikit‑learn) Experience building scalable ML pipelines using SageMaker Pipelines, Feature Store, Model Registry Solid understanding of MLOps: CI/CD, model versioning, experiment tracking, monitoring & drift detection Expertise in AWS services: S3, IAM, EC2, ECR, EKS/Lambda, CloudWatch Strong data engineering & feature engineering skills; SQL and large‑scale datasets Production‑grade software engineering practices (Git, APIs, Docker, automation) Experience working in secure, regulated environments Excellent communication skills Ability to lead design discussions and collaborate with stakeholders

Key Skills

Education

Bachelor's degree