New opportunity

Specialist, IT Application Engineer

Entegris · Singapore

About this role

The IT Application Engineer – R&D Application Support will manage modeling and simulation software applications on Linux hardware clusters and cloud infrastructure. The role focuses on installing, configuring, and deploying software packages, ensuring security, scalability, and reliability of HPC systems, and collaborating with data scientists to support scientific workloads. Key Responsibilities: Install and manage software packages on HPC and storage infrastructure; optimize HPC configurations for performance; collaborate with data scientists to deploy scalable simulation workloads; ensure security and reliability of cloud-based HPC systems; design networking solutions for license servers; deploy and maintain AI and simulation workflows on the cloud; monitor and automate SLURM cluster operations; and provide technical leadership in cloud and HPC management. Required Qualifications: 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; expertise in Linux-based cluster management (SLURM); proficiency in containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform, Ansible); knowledge of storage architectures (Lustre, GPFS, Ceph); and strong understanding of network architecture and security. Preferred Qualifications: Experience deploying AI-enabled engineering workflows integrating LLMs, simulation software, and optimization frameworks; exposure to Python-based scientific computing; and familiarity with Google Vertex AI, MLOps, and secure AI deployment practices.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

LinuxHPCCloud ComputingSLURMAzureGCPAWSDockerKubernetesTerraformCloudFormationPackerAnsiblePBSLSFLustreGPFSCephFlexLMDSLSPythonGoogle Vertex AIMLOps

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

HPC and Simulation Infrastructure Management
The core of this role is the installation, configuration, and optimization of modeling and simulation software on Linux-based HPC clusters. You must demonstrate proficiency in managing job schedulers like SLURM and distributed storage architectures (Lustre, GPFS, Ceph) to ensure high-performance scientific workloads.
Cloud-Native Engineering Workflows
You are expected to deploy and maintain simulation and AI-enabled workflows within cloud environments (Azure, GCP, AWS). This requires hands-on experience with infrastructure-as-code (Terraform, Ansible) and containerization (Docker, Kubernetes) to ensure scalability and reliability.
License Management and Network Security
A critical operational duty is the design of networking solutions for license servers (FlexLM, DSLS) and the enforcement of security best practices. You must balance providing researchers with seamless access to engineering software while maintaining strict identity, access management, and encryption standards.
Cross-Functional Collaboration and Technical Leadership
You will act as a bridge between IT infrastructure and R&D teams. Success depends on your ability to collaborate with data scientists to deploy scalable workloads, provide technical guidance on cloud systems, and document system designs for operational continuity.

Likely challenges to think through

Balancing Performance with Cost-Efficiency
Optimizing HPC cloud resources for cost while maintaining high-speed performance for scientific simulations is a common trade-off. Prepare by discussing how you monitor resource utilization and implement automated scaling or scheduling policies to prevent cloud overspend.
Integrating Legacy Simulation with Modern AI
The role requires integrating traditional simulation software with modern AI frameworks like Google Vertex AI. The challenge lies in ensuring compatibility between legacy licensing models and modern MLOps pipelines; prepare by highlighting your experience in bridging these two distinct technical ecosystems.
Managing Complex Multi-Cloud Environments
Operating across Azure, GCP, and AWS introduces significant architectural complexity regarding networking and security. Prepare by explaining your strategy for maintaining consistent security posture and infrastructure-as-code standards across heterogeneous cloud providers.

Practice your answers

Technical practice

SLURM Cluster Management and Automation

What to coverDescribe your experience in building and sustaining SLURM-based Linux clusters. Focus on how you handle job scheduling, resource allocation, and automation of cluster operations to ensure high availability for scientific workloads.

HPC Storage Architectures

What to coverExplain your familiarity with distributed file systems like Lustre, GPFS, or Ceph. Discuss the trade-offs between these systems when supporting high-throughput simulation and modeling applications.

Infrastructure-as-Code (IaC) in Cloud Environments

What to coverProvide examples of how you have used tools like Terraform, Ansible, or Packer to deploy and manage cloud-based HPC infrastructure. Emphasize how you ensure scalability and consistency across multi-cloud environments like AWS, Azure, or GCP.

Application Licensing and Network Design

What to coverDiscuss your experience managing license servers such as FlexLM or DSLS. Explain how you design secure networking solutions to ensure these license servers are accessible to HPC clusters while maintaining strict security protocols.

Containerization for Scientific Workloads

What to coverHow do you leverage Docker and Kubernetes to deploy simulation or AI workflows? Address the challenges of running containerized applications within an HPC environment versus traditional bare-metal or VM deployments.

Optimizing HPC Performance

What to coverDescribe your methodology for identifying and resolving performance bottlenecks in HPC configurations. Suggestion: Focus on how you balance compute speed with cost-effectiveness in a cloud-based HPC setup.

AI and MLOps Integration

What to coverExplain your approach to deploying AI-enabled engineering workflows, specifically integrating LLMs or optimization frameworks. Discuss how you manage model deployment and secure access in a research-oriented environment.

Cloud Security Best Practices

What to coverDetail your experience with identity and access management (IAM), encryption, and secure network design in the context of cloud-based HPC. How do you ensure compliance while providing researchers with the necessary access?

Behavioural practice

Collaborating with Data Scientists

What to coverDescribe a situation where you had to translate complex technical requirements from data scientists into a scalable HPC deployment. Focus on how you managed expectations and ensured the final infrastructure met their scientific needs.

Technical Leadership in Support Roles

What to coverProvide an example of a time you provided technical leadership or guidance to a team regarding cloud or HPC management. How did you document your procedures and ensure knowledge transfer to others?

Handling Vendor and Stakeholder Requirements

What to coverTell me about a time you collaborated with software vendors to deploy a custom application. How did you handle requirements capture and ensure the deployment was successful and secure?

Continuous Improvement and Innovation

What to coverGive an example of how you identified an inefficiency in an existing HPC or cloud workflow and implemented a solution to improve it. What was the impact on performance or cost?

Sharpen your knowledge

Topics to prepare

HPC Cluster Architecture and SLURM Operations
Review the principles of high-performance computing, specifically focusing on job scheduling, resource allocation, and cluster monitoring using SLURM. Exercise: Draft a plan for automating cluster health checks and resource optimization to ensure reliable simulation throughput.
Cloud-Native AI and Simulation Workflows
Understand the integration of simulation software with AI frameworks and MLOps pipelines in cloud environments. Exercise: Map out a secure deployment architecture that connects simulation data to Google Vertex AI, ensuring controlled access and efficient resource usage.
Enterprise Software Licensing and Networking
Study the architecture of license servers like FlexLM and DSLS within complex network environments. Exercise: Design a network topology that provides secure, low-latency access to license servers for distributed HPC nodes.

Skills to revise

Infrastructure-as-Code (Terraform/Ansible)
Practice defining and deploying cloud infrastructure and software configurations programmatically. Exercise: Create a Terraform script to provision a basic Linux-based compute cluster and use Ansible to automate the installation of a simulation software package.
Containerization for HPC
Develop proficiency in packaging simulation workloads using Docker and orchestrating them with Kubernetes. Exercise: Containerize a sample scientific application and configure a Kubernetes manifest to manage its resource requirements in a cloud environment.
Storage Architecture Management
Gain familiarity with distributed file systems such as Lustre, GPFS, or Ceph. Exercise: Compare the performance characteristics of these file systems for high-throughput simulation workloads and document a scenario where each would be the optimal choice.

Your preparation checklist

Prepare a technical walkthrough of HPC cluster management
Draft a summary of your experience building and maintaining SLURM-based Linux clusters. Be prepared to discuss specific challenges you solved regarding job scheduling, resource allocation, and performance optimization for scientific workloads.
Document your Infrastructure-as-Code (IaC) and containerization workflows
Create a brief overview of how you have used Terraform, Ansible, or similar tools to automate cloud infrastructure. Include examples of how you have deployed containerized applications using Docker or Kubernetes within an HPC or cloud environment.
Review your experience with license server management
Prepare to explain your process for managing commercial software licenses, specifically FlexLM or DSLS. Focus on how you ensure high availability and secure network access for these license servers in a distributed or cloud-based architecture.
Synthesize your AI and MLOps project experience
Prepare a case study highlighting your work with AI-enabled workflows, such as integrating LLMs or simulation frameworks. If applicable, describe your familiarity with Google Vertex AI or MLOps practices for secure model deployment.
Formulate strategic questions for the interviewers
Prepare thoughtful questions regarding the team's current cloud migration strategy, how they balance HPC performance with cost-optimization, and the specific types of scientific simulation software they currently support.

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