Supports modeling and simulation applications on Linux-based HPC and cloud infrastructure. Responsibilities include installing and optimizing software, managing SLURM clusters and licenses, deploying AI and simulation workflows, designing secure networking, automating infrastructure, optimizing cloud costs, and collaborating with data scientists, engineers, vendors, and infrastructure teams. The role also provides technical guidance, user support, training, documentation, and operational improvements across multi-cloud environments.
Job Title:
Specialist, IT Application EngineerJob Description:
Job Summary
Our team is looking for a highly skilled IT Application Engineer – R&D Application Support with expertise in Modeling and Simulation software applications running on Linux hardware clusters. The selected individual has expertise in installing, configuring, deploying, updating, and managing software application packages running in Linux based Cloud infrastructure, understanding various application licensing models, understanding networking protocols, network architecture, and security best practices such as identity and access management, encryption, and secure network design to ensure projects align with business goals, while driving innovation and continuous improvement.
Reports to: Senior Manager, IT Business Analysis
Key Responsibilities
- Install, and manage software packages that run on High-Performance Compute (HPC) and storage infrastructure that supports scientific workloads
- Optimize computing speeds of software by optimizing HPC configurations.
- Collaborate with data scientists and engineers to deploy scalable modeling and simulation workload in an HPC environment
- Ensure the security, scalability, and reliability of HPC systems in the cloud
- Work with infrastructure team to design networking solutions to various license servers based on FlexLM and other technologies
- Deploy and maintain AI and simulation workflows on cloud
- Maintain and allocate licenses for commercial software
- Optimize HPC cloud resources for cost-effective and efficient use
- Drive innovation and stay current with the latest in cloud services and industry standard processes
- Provide technical leadership and guidance in cloud and HPC systems management
- Monitor and automate slurm cluster operations
- Document system design and operational procedures
Requisite Criteria & Skills
- Experience supporting modeling and simulation software environments on Windows and Linux, including installing commercial and open-source simulation software suites, managing software licenses, and providing operational support Experience with building, deploying, and sustaining SLURM Linux based clusters.
- Experience with SaaS.
- Exposure to multi-cloud environments, including Azure, GCP, and AWS.
- Experience in an Agile development environment.
- Prior work with application and user support and training in a support role.
- Four or more years of experience supporting cloud, HPC, or enterprise application environments.
- Two or more years of hands-on experience with cloud service providers and related platform tools.
- Demonstrated expertise in cloud computing and cloud architecture.
- Working knowledge of containerization technologies, including Docker, Kubernetes, and cloud-based HPC solutions.
- Working knowledge of infrastructure-as-code tools such as Terraform, CloudFormation, Packer, or Ansible.
- Proficiency with job scheduling and resource management tools such as SLURM, PBS, or LSF.
- Knowledge of storage architectures and distributed file systems, including Lustre, GPFS, or Ceph.
- Strong understanding of network architecture and security best practices.
- Understanding of license-server technologies and requirements, including FlexLM and DSLS.
Preferred Qualifications
- Experience deploying AI-enabled engineering workflows that integrate large language models, simulation software, optimization frameworks, and HPC infrastructure.
- Exposure to Linux-native and Python-based scientific computing environments.
- Familiarity with Google Vertex AI, MLOps, model deployment, or AI platform operations
- Familiarity with secure AI deployment practices and controlled access to engineering software environments.
- Experience deploying custom software applications, collaborating with vendors, and supporting requirements capture and implementation.
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