Principal Solutions Architect, AI Data Infrastructure

Firmus Technology

Firmus Technologies is a global leader pioneering the development and operation of efficient AI infrastructure across Asia Pacific. 

Founded in Australia in 2019, our mission is to create the most efficient AI infrastructure by combining cutting-edge technology with a steadfast commitment to sustainability.

At Firmus, we are unique in our approach. We design, build, and operate a new class of digital infrastructure – the AI Factory. Through our model-to-grid technology approach, we have pushed the boundaries of multi-generational liquid cooling systems, energy management, AI software orchestration, and construction. For our customers, this approach allows us to make every watt count and deliver low-cost AI tokens globally.

Firmus AI Cloud

Our large-scale GPU cloud platform, Firmus AI Cloud, is purpose-built to deliver energy-efficient AI compute at scale to customers.

It empowers developers, enterprises, educational institutions, and government users to train and deploy AI models with unmatched efficiency and cost savings. With an ever-growing suite of services and applications, we are committed to delivering a cloud experience that is market-leading, proprietary, and built to scale.

Role Summary

We are seeking a Principal Solution Architect, AI Data Infrastructure to serve as Firmus’s subject-matter expert on everywhere data lives and moves across the AI fabric—storage, memory, and the caching layers that keep GPUs fully utilised. You will own the architecture, evaluation, and optimisation of the systems that feed our AI Factory and Firmus AI Cloud, from high-performance parallel and software-defined storage, through NVMe and next-generation memory fabrics, to the KV-cache and data-movement paths that determine real training and inference performance. In a business built on making every watt count, your job is to make every byte count with it.

You will operate as the trusted technical authority across both internal engineering teams and external customers – translating complex data-path decisions into measurable performance, capacity, and TCO outcomes. Whether validating a customer’s workload requirements or shaping our own platform roadmap, you’ll be the person who understands the principles deeply enough that specific vendors and brands are secondary to the architecture.

Key Responsibilities

You’ll own the reference architecture for the AI data path end to end—parallel and software-defined storage, NVMe and NVMe-oF, object and file systems, memory disaggregation and pooling, and the KV-cache and data-movement strategies that eliminate bottlenecks between storage, memory, and GPUs. You’ll evaluate, benchmark, and select technologies against real AI workloads, turning results into clear decisions on performance, capacity, power efficiency, and total cost of ownership.

Depending on the engagement, you’ll work externally as the trusted advisor to customers, sizing solutions, validating requirements, and resolving performance issues in production – or internally, shaping the platform roadmap and integrating storage and memory into our Kubernetes-based and bare-metal environments alongside compute and networking teams. Either way, you’ll bring the low-latency fabric expertise (RDMA, RoCEv2, InfiniBand) to connect it all, and establish the operational best practices for data protection, resilience, and lifecycle management that a large-scale cloud demands.

Skills & Experience

You’ll bring deep, vendor-agnostic expertise across the AI data path, with hands-on experience of one or more leading high-performance storage and memory platforms—systems in the class of WEKA, VAST Data, DDN, or Dell (PowerScale/PowerFlex)—understanding the underlying principles well enough that the specific brand is secondary. You’re fluent in NVMe and NVMe-oF, parallel and software-defined storage, object and file systems, and the low-latency network fabrics (RDMA, RoCEv2, InfiniBand) that link data to GPUs, and you track next-generation memory directions such as CXL, memory disaggregation, and KV-cache optimisation for LLM workloads.

Just as important is benchmarking discipline – you can design and run workload-driven evaluations across bare-metal, virtualised, and containerised environments and translate the numbers into architecture and business cases. You’ve done this at scale, integrating storage and memory into GPU clusters (DGX/HGX and similar) and reasoning about the CAPEX, server-count, and power trade-offs that come with it. Typically this comes with 10+ years in storage, memory, HPC, or AI infrastructure roles.

Communication rounds it out. You can advise a C-level customer, author a clear reference architecture or whitepaper, and mentor engineers with equal ease, and you’re comfortable in high-ambiguity environments where the right architecture has to be created rather than copied.

Key Competencies

You bring deep command of the full AI data path – storage, memory, and cache – and back it with rigorous, workload-driven benchmarking. You think in systems, reasoning fluently across compute, network, and data, and you stay pragmatic and vendor-agnostic, optimising for outcomes rather than badges. You operate as a single-threaded owner, driving to the outcome, and you communicate with equal ease across engineering teams and executive audiences.

Location & Reporting

• Singapore
• Reporting to the Chief Technology Officer (CTO) in the interim

Employment Basis

Full-time

Diversity

At Firmus, we are committed to building a diverse and inclusive workplace. We encourage applications from candidates of all backgrounds who are passionate about creating a more sustainable future through innovative engineering solutions.

Join us in our mission to revolutionize the AI industry through sustainable practices and cutting-edge engineering. Apply now to be part of shaping the future of sustainable AI infrastructure.