- Core Responsibilities (AI/ML Python AWS GenAI)
- Design and implement end-to-end AI/ML and Generative AI solutions using Python including model training evaluation optimization and deployment.
- Build and maintain cloud native applications on AWS using services such as Lambda ECS/Fargate S3 API Gateway DynamoDB RDS/Aurora SageMaker and Bedrock.
- Develop high performance Python microservices (FastAPI/Flask) enabling scalable data pipelines model inference and real time analytics.
- Architect and operationalize RAG pipelines embeddings vector databases and LLM powered automation (chatbots summarization semantic search anomaly detection).
- Implement CI/CD pipelines (GitHub/GitLab/CodePipeline) and infrastructure as code (Terraform/CloudFormation) for reliable automated deployments.
- Build robust MLOps workflows including model versioning containerized training/inference automated retraining monitoring and performance tuning.
Core Responsibilities (AI/ML Python AWS GenAI) Design and implement end-to-end AI/ML and Generative AI solutions using Python including model training evaluation optimization and deployment. Build and maintain cloud native applications on AWS using services such as Lambda ECS/Fargate S3 API Gatew...
- Core Responsibilities (AI/ML Python AWS GenAI)
- Design and implement end-to-end AI/ML and Generative AI solutions using Python including model training evaluation optimization and deployment.
- Build and maintain cloud native applications on AWS using services such as Lambda ECS/Fargate S3 API Gateway DynamoDB RDS/Aurora SageMaker and Bedrock.
- Develop high performance Python microservices (FastAPI/Flask) enabling scalable data pipelines model inference and real time analytics.
- Architect and operationalize RAG pipelines embeddings vector databases and LLM powered automation (chatbots summarization semantic search anomaly detection).
- Implement CI/CD pipelines (GitHub/GitLab/CodePipeline) and infrastructure as code (Terraform/CloudFormation) for reliable automated deployments.
- Build robust MLOps workflows including model versioning containerized training/inference automated retraining monitoring and performance tuning.
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