Senior Forward Deployed Engineer (AI Infrastructure)

TL;DR

Senior Forward Deployed Engineer (AI Infrastructure): Building and operating large-scale enterprise data infrastructure for AI training, inference, and real-time analytics with an accent on distributed systems, high-performance storage, networking, and Linux internals. Focus on co-engineering customer deployments, solving complex infrastructure issues, developing automation and API integrations, and translating operational requirements into core product improvements.

Location: Australia; ability to travel or work on-site at customer locations as required, with up to 25% travel.

Company

hirify.global develops enterprise data platform infrastructure for capturing, managing, protecting, and analyzing massive datasets used in AI and real-time analytics.

What you will do

• Work directly with customer architects, operators, and developers to design and operate strategic deployments.
• Optimize infrastructure throughput, debug complex distributed systems issues, and develop solutions for large-scale workloads.
• Translate customer requirements and operational insights into precise technical requirements for core product teams.
• Build custom API endpoints, automation tools, and scripts to unblock critical customer workflows.
• Represent customer technical needs in product roadmap discussions and coordinate with engineering teams on fixes and releases.

Requirements

• 5+ years of experience in a highly technical engineering role with customer-facing collaboration.
• Deep knowledge of distributed systems, high-performance file systems, storage protocols, and performance tuning.
• Strong understanding of InfiniBand, RoCE, and 100GbE+ networking.
• Advanced knowledge of Linux architecture, memory management, and kernel mechanics.
• Proficiency in Python and C/C++, including debugging and contributing to complex enterprise codebases.
• Ability to travel or work on-site at customer locations as required, up to 25%.

Nice to have

• Experience operating large-scale AI training environments or HPC clusters.
• Experience with Docker, Kubernetes, PyTorch, or TensorFlow.

Culture & Benefits

• Direct impact on infrastructure supporting advanced AI systems.
• Close collaboration with customer engineering teams and internal product developers.
• Exposure to complex, fast-evolving distributed infrastructure technologies.
• Role includes global engineering collaboration and customer-facing technical ownership.