ROLE_DESCRIPTION -
Senior AWS ML Engineer with 8+ years of experience in
Design, build, and deploy scalable Machine Learning solutions on AWS using SageMaker, ensuring high performance, reliability, and security.
Develop and maintain end-to-end ML pipelines, including data preparation, model training, hyperparameter tuning, deployment, and monitoring.
Collaborate with data scientists, engineers, and business stakeholders to operationalize ML models, automate workflows, and drive business outcomes through AI/ML solutions.
Detailed Technical skills -
Amazon SageMaker Expertise – Strong experience with SageMaker Studio, Training Jobs, Pipelines, Model Registry, Feature Store, and Endpoint Deployment.
Machine Learning & Deep Learning – Hands-on experience building, training, tuning, and deploying ML/DL models using Scikit-learn, XGBoost, TensorFlow, and PyTorch.
AWS Cloud Services – Proficiency in S3, EC2, IAM, Lambda, ECR, ECS/EKS, CloudWatch, and Step Functions for ML workloads.
MLOps & CI/CD – Experience implementing automated ML pipelines, model versioning, deployment automation, monitoring, and retraining workflows.
Python Programming – Strong coding skills with Python and ML libraries such as Pandas, NumPy, Scikit-learn, and Boto3.
Data Engineering & Analytics – Experience with data ingestion, transformation, and processing using AWS Glue, Athena, Redshift, and EMR.
Model Monitoring & Governance – Expertise in data drift detection, model performance monitoring, explainability, and governance using SageMaker monitoring capabilities.
Solution Architecture & Leadership – Ability to design scalable end-to-end ML solutions on AWS, lead technical discussions, mentor teams, and collaborate with business stakeholders.
Skills: Digital : Python~Digital : Machine Learning
Experience Required: 6-8
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