Design, deploy, and manage secure and scalable AWS infrastructure for healthcare and AI/ML workloads.
Build and maintain automated deployment pipelines using Infrastructure as Code (Terraform, CloudFormation). Support ML model training and inference infrastructure (e.g., Sage Maker, EC2 GPU instances, Lambda). Implement and monitor HIPAA-compliant cloud environments with strong focus on data privacy and security. Optimize storage and compute resources for performance and cost (e.g., S3, EBS, EFS, Auto Scaling). Collaborate with data scientists, ML engineers, and backend developers to streamline data processing and model deployment. Maintain documentation for infrastructure design, operations, and disaster recovery procedures. Key Skills and Qualifications:

Bachelors or Masters in Computer Science, Information Systems, or related field.
Experience supporting AI/ML pipelines in production, ideally using AWS services like Sage Maker or Batch. Solid understanding of healthcare data standards (e.g., HL7, FHIR, HIPAA compliance requirements). Proficiency in Python, Bash, or other scripting languages. Experience with containerization (Docker) and orchestration tools (ECS, EKS, or Kubernetes). Familiarity with CI/CD pipelines and tools such as GitHub Actions, Jenkins, or GitLab. Knowledge of infrastructure monitoring tools like Prometheus, CloudWatch, or Datadog. Preferred Skills & Qualifications (Nice to Have):

AWS Certifications (e.g., Solutions Architect, DevOps Engineer, Machine Learning Specialty).
Experience with big data and ETL tools in AWS (e.g., Glue, Athena, Redshift). Exposure to AI/ML frameworks (TensorFlow, PyTorch) and data versioning tools (e.g., DVC). Knowledge of medical imaging, genomics, or clinical data integration is a strong plus.

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AWS Cloud Engineer