AWS & SageMaker MLOps Expert

Role: AWS & SageMaker MLOps ExpertLocation: Pune (Onsite)Job type: Contract/Full timeExperience Level: 5+ years in DevOps/MLOps/Cloud Engineering with 3+ years specifically on AWS SageMaker and proven track record of production ML deployments at scaleCertifications (Bonus): AWS Certified Machine Learning – Specialty, AWS Certified Solutions Architect (Associate/Professional)- AWS SageMaker Expertise: Hands-on experience with SageMaker Unified Studio, Pipelines, Model Registry, Feature Store, Endpoints (real-time/batch), Experiments, Debugger, Clarify, and Model Monitor for comprehensive ML lifecycle management. - ML Lifecycle Management: End-to-end MLOps including data preprocessing, model training, hyperparameter tuning, versioning, deployment strategies (blue/green, canary, A/B testing), monitoring, and automated retraining workflows. - Programming Proficiency: Strong Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow, XGBoost), boto3 for AWS SDK, SQL for data querying, and Git/GitHub for version control and collaboration - AWS Cloud & Infrastructure: Deep knowledge of AWS services: EC2, S3, Lambda, Step Functions, ECS/EKS, IAM, VPC, CloudWatch, EventBridge, Glue, Athena, and Infrastructure as Code (CloudFormation, AWS CDK, or Terraform) - CI/CD & Automation: Building/deploying ML pipelines with GitHub Actions/GitLab CI, automated testing frameworks, and deployment automation with proper rollback strategies - Monitoring & Observability: Experience with SageMaker Model Monitor for data quality, model drift, bias detection, and explainability (SageMaker Clarify, SHAP, LIME), CloudWatch metrics/alarms, and custom monitoring dashboards - Advanced ML Operations: SageMaker Feature Store for feature management, SageMaker Automatic Model Tuning, distributed training (SageMaker Training Jobs with multi-GPU/multi-node), and model optimization techniques (quantization, pruning)

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