Senior High Performance Computing Engineer

Roles & Responsibilities:

• Implement, and manage cloud-based infrastructure that supports HPC environments that support data science (e.g. AI/ML workflows, Image Analysis).
• Collaborate with data scientists and ML engineers to deploy scalable machine learning models into production.
• Ensure the security, scalability, and reliability of HPC systems in the cloud.
• Optimize cloud resources for cost-effective and efficient use.
• Keep abreast of the latest in cloud services and industry standard processes.
• Provide technical leadership and guidance in cloud and HPC systems management.
• Develop and maintain CI/CD pipelines for deploying resources to multi-cloud environments.
• Monitor and fix cluster operations/applications and cloud environments.
• Document system design and operational procedures.

Basic Qualifications:

• Master's degree with a 4 - 6 years of experience in Computer Science, IT or related field with hands-on HPC administration OR
• Bachelor's degree with 6 - 8 years of experience in Computer Science, IT or related field with hands-on HPC administration OR
• Diploma with 10-12 years of experience in Computer Science, IT or related field with hands-on HPC administration
• Demonstrable experience in cloud computing (preferably AWS) and cloud architecture.
• Experience with containerization technologies (Singularity, Docker) and cloud-based HPC solutions.
• Experience with infrastructure-as-code (IaC) tools such as Terraform, CloudFormation, Packer, Ansible and Git.
• Expert with scripting (Python or Bash) and Linux/Unix system administration (preferably Red Hat or Ubuntu).
• Proficiency with job scheduling and resource management tools (SLURM, PBS, LSF, etc.).
• Knowledge of storage architectures and distributed file systems (Lustre, GPFS, Ceph).
• Understanding of networking architecture and security best practices.

Preferred Qualifications:

• Experience supporting research in healthcare life sciences.
• Experience with Kubernetes (EKS) and service mesh architectures.
• Knowledge of AWS Lambda and event-driven architectures.
• Exposure to multi-cloud environments (Azure, GCP).
• Familiarity with machine learning frameworks (TensorFlow, PyTorch) and data pipelines.
• Certifications in cloud architecture (AWS Certified Solutions Architect, Google Cloud Professional Cloud Architect, etc.).
• Experience in an Agile development environment.
• Prior work with distributed computing and big data technologies (Hadoop, Spark).
• Professional Certifications (please mention if the certification is preferred or mandatory for the role):
• Red Hat Certified Engineer (RHCE) or Linux Professional Institute Certification (LPIC)
• AWS Certified Solutions Architect – Associate or Professional