Ahmed Qura

About

Ahmed Qura is a senior mechanical engineer whose career has been defined by a single, demanding question: how can we optimize the maintenance cost and monitor the failures, and what will it take to get there? Over 10 years in practice across international companies, he has developed a specialty in failure modes analysis that has made him a trusted figure on some of the country's most complex projects.

Qura completed his Bachelor of Engineering with before undertaking a Master in artificial intelligence, at University of Essex. his master research examined the implementation of AI in maintenance and reliability — specifically, how to reduce the human error in maintenance process and identifing failure modes when multiple failure types interact.

Since completing his first masters degree, Qura has published two papers concluding his researcha on integrating maintenance and AI to improve the maintenance and reliability process. His portfolio spans CMMS, Rotating equipment manufacture and maintaine those equipment, and optimizing cost and time for maintenance using reqliability data.

She is a Fellow of Engineers Australia — a status that reflects sustained contribution to the profession — and holds a Chartered Professional Engineer designation. In addition to her practice, she has contributed to structural engineering standards development and serves on the technical committee of a national engineering body examining load assessment methodologies for the Australian built environment.

Dr Sharma joined Praxa because she had repeatedly encountered a problem she did not have a good solution to. Structural engineers making conservative-by-default decisions — not because conservatism was warranted, but because they lacked a reliable way to distinguish contexts where conservatism added real safety value from contexts where it simply increased cost without proportionate benefit. The judgment required to make that distinction is not taught formally and does not derive mechanically from codes and standards. It is built through practice. Dr Sharma believed that building it into a tool was both possible and necessary.

Domain Expertise

• Structural load assessment — evaluating actual and anticipated load conditions across varying structure types, use cases, and environmental contexts
• Failure mode analysis — systematic identification of the pathways through which structures may fail, including progressive and non-obvious failure mechanisms
• Dynamic load response — assessment of structural behaviour under wind, traffic, seismic, and industrial dynamic loading conditions
• Heritage building risk assessment — structural evaluation of buildings where original documentation is incomplete and modification history is uncertain
• Seismic analysis — assessing structural vulnerability and designing appropriate intervention strategies in seismically active zones
• Foundation behaviour in variable soils — analysis of how foundation performance changes under varying subsoil conditions, including reactive and liquefiable soils
• Regulatory compliance strategy — navigating Australian structural engineering standards where code requirements and practical engineering judgment must be reconciled

The Problem Engineering AI Has Not Solved

Dr Sharma's critique of current engineering software and AI tools is precise. The tools are good at calculation. They are poor at judgment. And in structural engineering, the gap between calculation and judgment is where consequential decisions are made.

A structural analysis package can tell you the calculated stress at every node in a finite element model. It cannot tell you whether the model's assumptions are appropriate for the actual condition of this structure, with this particular construction history, in this particular loading environment. That judgment is the engineer's contribution — and it is the contribution that current tools do nothing to support, inform, or improve.

The result is a profession that produces technically sophisticated output but exercises its most important judgment in an unstructured, highly variable way. Two engineers of different experience levels, working with the same analytical tools on the same structure, will often reach different conclusions — not because one has made a calculation error, but because they have applied different judgment to the same set of facts. The more experienced engineer's judgment is usually better. It is also largely inaccessible to the less experienced one.

Engineering AI, as Dr Sharma has observed it, has focused on accelerating the calculation layer — automating what was already fast and leaving the judgment layer entirely unaddressed. What Praxa is building does the opposite: it leaves the calculation to the tools that already do it well, and focuses on encoding the practitioner judgment that currently cannot be systematically transmitted from senior to junior engineers.

Collaboration with Praxa

Dr Sharma's role at Praxa is to make her load assessment and failure analysis judgment operational. The work required her to do something that most engineers are not trained to do: articulate not just what they conclude, but how they reason — the factors they attend to, the conditions under which those factors change, and the contextual signals that tell an experienced engineer when a standard approach is insufficient.

Working with Praxa's product team, she has structured that reasoning into frameworks that can function as the backbone of an engineering AI tool — one that does not replace the engineer's analysis but brings experienced judgment to bear on how that analysis should be scoped, sequenced, and qualified.

The collaboration is ongoing. As the product encounters new structural contexts, Dr Sharma's involvement ensures that the underlying judgment framework adapts to reflect the actual complexity of engineering practice — not a simplified version of it.

For more on why Praxa structures product development around domain practitioners rather than data, read: Why domain expertise is the missing ingredient in AI product development.