Job Summary

About the Role

We are looking for a Platform Engineer or Modelling & Simulation Engineer to help build and support the digital infrastructure for our new modelling, simulation and agentic AI projects.

The ideal candidate does not need to be an expert in every area. We are looking for someone with strong fundamentals in either platform/infrastructure engineering (Linux, automation, cloud) or modelling and simulation engineering (building, running, or supporting M&S models and computational workflows), along with an interest in supporting advanced research workflows.

Key Responsibilities

The list below are examples of the responsibilities of the role, but not all responsibilities are to be taken by the single person.

• Design, build, and maintain hybrid infrastructure (on-premise, cloud, and containers) for modelling, simulation, and scientific computing workloads. 
• Develop, build, and validate modelling and simulation models and computational workflows (e.g., discrete-event, agent-based, physics-based, or systems simulation). 
• Automate infrastructure, operational, and simulation workflows (execution, calibration, results analysis) using scripting, infrastructure-as-code, and DevOps practices. 
• Work with researchers, engineers, cybersecurity teams, and stakeholders to translate requirements into practical technical solutions. 
• Contribute to CI/CD pipelines, deployment automation, and documentation, and help enable secure, federated modelling and simulation workflows across systems and teams. 
• Deploy and maintain local AI infrastructure, including GPU-based compute resources, and support the integration and reliability of agentic AI systems and tooling. 

Required Skills and Experience

• Degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience. 
• Experience in platform engineering, DevOps, cloud infrastructure, Linux administration, SRE, or research computing — OR experience in modelling and simulation engineering, computational modelling, or scientific/engineering simulation development. 
• For platform-focused candidates: good working knowledge of Linux systems, basic networking, and at least one cloud platform. For modelling and simulation-focused candidates: good working knowledge of building, running, or validating simulation or computational models. 
• Some experience with automation or scripting (Bash, Python, Terraform, Ansible), containers, CI/CD, or infrastructure-as-code, or with modelling and simulation software such as MATLAB/Simulink or Python-based scientific computing. 
• Awareness of security good practices, such as access control, encryption, patching, logging, and secure remote access. 
• Ability to communicate clearly with technical and non-technical stakeholders, and an interest in learning AI/ML infrastructure concepts such as local model deployment or agentic AI systems (no expertise required). 

Preferred Skills and Experience

• Experience with AWS services (EC2, S3, IAM, VPC, Systems Manager, AWS Batch) or container/orchestration platforms such as Docker, Kubernetes, or ECS. 
• Experience supporting scientific computing, modelling and simulation, AI/ML, HPC, or research-oriented environments — including hands-on development or validation of M&S models (e.g., discrete-event, agent-based, physics-based, or systems simulation), GPU infrastructure, or AI model serving frameworks (e.g., vLLM, Ollama, Triton Inference Server). 
• Familiarity with secure or federated workflows, distributed computing, or cross-team collaboration. 
• Familiarity with modelling and simulation platforms or frameworks (e.g., MATLAB/Simulink, AnyLogic, Simio, DEVS-based tools, or custom simulation frameworks) and with verification and validation (V&V) practices for simulation models. 
• Experience with CI/CD tools, Git-based workflows, or infrastructure automation, and any relevant certifications in cloud platforms, Linux, Kubernetes, DevOps, or cybersecurity.