AI Infrastructure Engineer

TL;DR

AI Infrastructure Engineer: Designing, building, and maintaining scalable AI infrastructure, including compute resources, storage solutions, and networking configurations with an accent on efficient AI model development, deployment, and operation. Focus on performance optimization, seamless operation of AI models in production, and ensuring AI infrastructure and models comply with relevant security and regulatory requirements.

Location: Must be based in Edinburgh, Scotland and work from the office at least three days per week (3:2 hybrid policy)

Company

hirify.global is a US$69 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets.

What you will do

• Design, build, and maintain scalable and efficient AI infrastructure.
• Develop and implement processes for deploying, monitoring, and managing AI models in production environments.
• Create and maintain automation scripts and tools for AI model training, testing, evaluation, and deployment in a continuous integration / continuous delivery (CI/CD) pipeline.
• Collaborate with data scientists, engineers, and other stakeholders to ensure smooth operation of AI systems and provide support as needed.
• Continuously monitor and optimize AI infrastructure and models for performance, scalability, utilization, and reliability.
• Ensure AI infrastructure and models comply with relevant security and regulatory requirements.

Requirements

• Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field.
• 8+ years of experience in software engineering, DevOps, or a related field.
• Strong background in computer systems, distributed systems, and cloud computing.
• Proficient in Linux system administration, including package management, user/group management, file system navigation, shell scripting (e.g. Bash), and system configuration (e.g., systemd, networking).
• Proficiency in programming languages such as Python, Java, or C++.
• Experience with AI-specific infrastructure and tools (e.g., NVIDIA GPUs and CUDA).
• Experience with managing high-performance computing (HPC) clusters, including job scheduling, resource allocation, and cluster maintenance.

Nice to have

• Familiarity with AI and machine learning frameworks (e.g., PyTorch).
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure).
• Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).
• Experience with monitoring and logging tools (e.g., Prometheus, Grafana).

Culture & Benefits

• Opportunities for career advancement and personal development.
• Access to a diverse range of training programs.
• Performance-based rewards that celebrate your achievements.
• Flexibility with a hybrid work model (3:2) that blends home and office life.
• Electric car salary sacrifice scheme.
• Life insurance.