Artificial Intelligence Engineer

Information Technology, Telecommunications, and Security

Job not Influenced by AI

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level of automation


Our comments:

The role of an Artificial Intelligence Engineer lies at the core of AI development and optimization. Paradoxically, despite working in the field of AI, this profession is minimally affected by AI itself due to its inherent complexity. Crafting algorithms and fine-tuning AI models necessitates a profound understanding, creativity, and intuition that current AI technologies cannot replicate.

Additionally, effective collaboration with other teams to integrate AI solutions calls for interpersonal and coordination skills, further underscoring the irreplaceable human element in this role. While AI excels in learning and adaptation, the creation and ongoing enhancement of AI systems remain firmly within the realm of human expertise.

This section reviews the 3 main tasks associated with the job studied and assesses the potential level of automation induced by AI ('AI Automation Impact').
The modeling uses 8 criteria detailed on the 'Methodology' page.
Tasks AI Automation Impact
Design machine learning algorithms Low
Optimize existing AI models Low
Collaborate with interdisciplinary teams to integrate AI into applications Moderate
Through our research, we have identified two pivotal categories of skills that will be impacted by AI-driven automation :
  • 'At-risk skills,' which are likely to become obsolete due to their susceptibility to automation
  • 'Future-proof skills', which are projected to retain their value and resist automation, thereby ensuring their relevance in the forthcoming job market.
Let's have a look at the future of your skills for this job :
At-risk Skills
Program in a specific computer language With the rapid evolution of AI and the emergence of new tools and platforms, being confined to a specific language can be limiting. Moreover, many emerging AI frameworks are making tasks easier without requiring deep programming in a specific language
Draft a Technical Requirement Specification (TRS) Rather than relying on rigid, detailed documents like TRS, many AI teams are adopting more flexible approaches that allow for rapid changes and continuous iterations. The focus is on rapid prototyping, testing, and adjustments, rather than heavy documentation. This skill isn't about to become obsolete immediately, but its relevance may decrease as working methodologies evolve in the AI field.
Future-proof Skills
Design machine learning algorithms This is the cornerstone of any role in artificial intelligence. The ability to understand, design, and improve algorithms is fundamental and will continue to be relevant as long as the AI field exists.
Collaborate with interdisciplinary teams AI finds applications in a multitude of fields, from medicine to automotive to finance. The ability to collaborate with experts from other disciplines is crucial to ensure AI solutions are relevant, ethical, and meet the real needs of users.

How does AI impact this job type ?
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