Key Responsibilities

Design, implement, and maintain end-to-end MLOps pipelines on AWS, including CI/CD automation for model training, validation, deployment, and retraining, using services like SageMaker, CodePipeline, CodeBuild, and Step Functions.

Support the Generative AI platform by operationalizing AWS Bedrock workflows, including RAG pipelines, vector databases (e.g., via OpenSearch or Pinecone integrations), Lambda functions, and agentic systems—ensuring scalability for large-scale data processing like historical test plans and news article summarization.

Enable classical ML initiatives by deploying and monitoring models built with XGBoost, Scikit-learn, and NLP architectures (e.g., RNNs/LSTMs) on AWS infrastructure, incorporating drift detection for anomaly tracking in sensor data and competitor pricing monitoring.

Manage infrastructure as code (IaC) using Terraform or CloudFormation to provision and optimize AWS resources, such as EC2 i...