AWS Cloud Engineer

The Opportunity:

We are looking for a Cloud Engineer with deep expertise in AWS to support our AI/ML-driven healthcare platform. You will play a key role in architecting and maintaining cloud infrastructure that powers critical healthcare applications, large-scale data pipelines, and machine learning workflows. This is a unique opportunity to contribute to cutting-edge technology that advances precision medicine, diagnostics, and patient care.

Key Responsibilities:

• 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:

• Bachelor s or Master s in Computer Science, Information Systems, or related field.
• 3+ years of hands-on experience with AWS (EC2, S3, VPC, RDS, IAM, CloudWatch, etc.).
• 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.