Machine Learning Engineer

• Job Description
• 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.

• Roles & Responsibilities
• 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.

Generic Managerial Skills, If any

• • Strong leadership and stakeholder management skills, with experience leading cross-functional teams and driving delivery excellence.
• • Proven effective communication with business and technical stakeholders.