Bridging the skills gap in Edge AI

Every transformative technology starts as a specialized ability—which also applies to artificial intelligence (AI). AI started as a laboratory and data center solution, yet it quickly became the driving force behind modern productivity applications and intelligent consumer products.

The AI adoption in different industries has surged but the supply of qualified professionals remains insufficient to meet this demand. A recent Gartner study reveals that 82% of CIOs believe the AI skills gap is hampering their ability to meet business goals.

Cloud computing provides AI with plenty of computing power as well as established frameworks and centralized data storage but there is still a shortage of expert professionals. Now imagine the situation at the edge- where resources are limited, operational requirements become more demanding and mistakes result in higher expenses.

Edge AI Raises the Stakes

Unlike the cloud, Edge AI operates on resource-starved devices, often disconnected, processing data locally in real time under strict power, memory, and hardware constraints. It demands a rare fusion of skills—machine learning, embedded systems, model compression, and real-time processing—making the talent shortage even more acute.

India’s Edge AI Boom

The deployment of AI shifts from faraway cloud computing centers to device edges where it operates instinctively with immediate responsiveness at local levels. The Edge AI market in India experienced 832.9 million US dollars in revenue during 2024 and analysts predict it will expand at 28.3 percent annual growth rate to reach 3.65 billion US dollars by 2030 (Grand View Research). The market has started to understand that AI operations based on cloud services create performance and privacy constraints, especially when real-time intelligence is required for autonomous vehicles, medical devices and industrial systems. 

The market growth is also driven by the advancement of edge-specific hardware because chip manufacturers such as Silicon Labs, NXP, Synaptics and NVIDIA produce powerful efficient AI processors. The software segment has become the fastest-growing market sector because developers can now use pre-trained models like ModelNova and access ready-made SDKs that reduce development barriers.

Finally, the combination of increasing data protection laws and customer requirements for rapid intelligent services is driving India's Edge AI transformation which has evolved beyond innovation into an essential business need.

Why the Skills Gap Exists

Despite skyrocketing demand, the Edge AI talent pool remains shallow. This is largely because Edge AI sits at the intersection of three traditionally separate domains: machine learning, embedded systems, and hardware optimization—rarely combined in conventional engineering education.

The fragmented nature of the edge ecosystem compounds the problem. Unlike the cloud’s standardized infrastructure, edge devices vary widely in processing capabilities, memory, and power budgets. Optimizing models for such diverse platforms requires highly specialized, device-specific knowledge.

Moreover, Edge AI development tools are often vendor-specific and poorly documented, making them less accessible than mature cloud frameworks like TensorFlow and PyTorch. This steepens the learning curve, especially for developers new to embedded systems.

AI education itself hasn't kept pace. Most training programs still emphasize cloud-based models, overlooking critical skills like real-time inference, model compression, and deployment on constrained devices. As a result, companies are forced to invest heavily in upskilling engineers on the job. A Deloitte–NASSCOM report stated that the AI market in India is expected to grow at 25–35 percent, potentially signaling a widening demand-supply gap in the talent pool. Over the past year, 43 percent of the Indian workforce across sectors have used AI in their organizations. Additionally, about 60 percent of workers and 71 percent of Gen Z professionals recognize that acquiring AI skills can significantly enhance their career prospects.

High-Demand Skills for Edge AI

The following competencies are crucial for Edge AI talent:

1.      Embedded Systems Development

Proficiency in embedded programming, real-time operating systems (RTOS), and hardware interactions is essential to run models efficiently on microcontrollers and edge processors. Of the $ 185 billion global industry, 26% of the embedded systems users use embedded AI in their applications.

2.      AI/ML Model Design & Optimization

Engineers must master techniques like quantization, pruning, and knowledge distillation to compress models while preserving accuracy. As per an EY India report, 81% of Indian organizations face a shortage of skilled AI/ML engineers, especially those with hands-on experience in model compression and deployment.     

3.      Hardware Acceleration

Familiarity with acceleration frameworks like TensorRT, OpenVINO, and Edge TPU tools is vital for optimizing performance and power efficiency.

4.      Edge Deployment & Integration

Experience deploying AI models in real-time systems, managing data pipelines, and ensuring cloud-edge interoperability, often using containerization and lightweight MLOps.

5.      Computer Vision & Signal Processing

Many edge use cases involve visual and audio data. Skills in OpenCV, ONNX, and DSP frameworks are key to building efficient AI for surveillance, voice assistants, and smart devices.

6.      Chipset-Specific Optimization

Platforms like NVIDIA Jetson and Qualcomm Snapdragon require developers adept at tailoring models for these chipsets to maximize efficiency and performance.

7.      Security & Privacy

Edge deployments often operate outside secure environments. Expertise in encryption, secure boot, model watermarking, and federated learning is critical to protect sensitive data and IP. According to a PWC study 69% of CEOs in India have moderate to high trust in having AI (including GenAI) embedded into key processes in their companies

Tools Helping Close the Gap

Fortunately, new tools and platforms are simplifying Edge AI development. Solutions like Edge Impulse and ModelNova are helping developers deploy models more easily, abstracting some complexities of constrained devices. Much like no-code tools democratized app development, these platforms are lowering entry barriers to Edge AI.

Edge AI is only as powerful as the people behind it

As Edge AI moves from buzzword to business-critical technology, the industry faces a pressing challenge: the widening gap between ambition and execution. The potential is immense, but realizing it depends on cultivating a new generation of talent equipped to navigate the complex intersection of AI, embedded systems, and real-time computing.

Closing this gap requires more than tools—it demands sustained investment in skills, collaboration, and ecosystems that empower developers to innovate without barriers. The future of Edge AI won’t be shaped by technology alone, but by the engineers, creators, and thinkers who bring that technology to life. Ensuring they have the knowledge, resources, and platforms they need is what will ultimately determine how far—and how fast—Edge AI can truly go.

By Rajesh Subramaniam, CEO & Founder of embedUR