A computer vision engineer aims to help computers ‘see’ through deep/machine learning and mathematical architectures in code.
Computer vision engineering makes the computer more sensitive and capable of recognizing objects to provide proper outputs, a step forward to implement human intelligence into the machine.
Machines must recognize and process and then act according to the observation. Computer vision can be classified into broad steps:
- Object classification: The first step is to train the machine on a specific database of objects and help it understand which objects fall under which category and how to differentiate between them.
- Object identification: With the database of relevant objects, it must recognize them when it flashes in front of it and responds in a certain manner as specified.
Understanding Computer Vision Talent through a Complex Multi-Disciplinary Lens
Computer vision engineers use software to handle the processing and analysis of large data populations to support the automation of predictive decision-making through visuals. They use large sums of data and statistics to complete complex tasks and supervise learning.
With experience with image recognition, machine learning, networking and communication, artificial intelligence, computations, machine learning, data science, and image/video segmentation, they get to work closely with other personnel in computer science.
Their tasks involve skills dependent on linear algebra math libraries and a foundational understanding of algorithms and mathematical processes. They will have software skills in database management, development environment, and component or object-oriented software and programming languages.
They also need analytical and critical-thinking skills so they can analyze results to make accurate conclusions. Logical thinking, clear reasoning, and being detail-oriented are critical skills because of the amount of research in the short deadlines and programming-related work required.
Computer vision engineers are required to have the ability to:
- Develop image analysis algorithms – For instance, algorithms allow programs to recognize and classify images.
- Develop deep learning architectures to solve problems – Deep learning, a sector of AI, can create robust image recognition or video analysis models.
- Create platforms for image processing and visualization – Computer vision engineers assist developers of the hosts of computer vision models, which involves designing apps, websites, or devices that will run computer vision models.
- Use knowledge of computer vision libraries – They must be comfortable with libraries specific to the computer vision task at hand as they program and code to create computer vision models.
- Understand dataflow programming – It is a programming feature that models a program as a directed graph of the data flowing between operations. It involves implementing dataflow principles and architecture.
Computer Vision Role Outlook
Careers in AI and ML are increasing as companies’ need for such engineers proportionally increases. As per the US Bureau of Labor Statistics, jobs for computer and information research scientists are expected to grow by 15% between 2019 and 2029, which is greater than other occupations.
Twenty years later, computer vision will become a commodity component inevitable in any consumer durable product. Application-specific analytics and intelligence will get added to the devices, including textual, audio, visual, and sensor analytics.
Due to the growing number of computer vision companies building cutting-edge hardware and software, this occupation is new for many people. It is a niche field requiring highly specialized experts. However, the shortage in computer vision talent is having talent management rethinking their strategies primarily because computer vision talent is in short supply.
Overcoming Talent Acquisition Challenges
As more industries deploy innovative use cases, there is a huge demand spike for ‘computer vision’ talent across IT & software, healthcare & medical devices, retail, automotive, and semiconductor. The market is expected to grow at a CAGR of 7.8% by 2026.
Talent management teams strategically plan and meet the growing demand for computer vision talent. Microsoft, Google, Philips, Walmart, Amazon, Bosch, Intel, AMD, and Medtronic are top companies hiring computer vision talent.
However, talent management teams cannot overcome the challenges related to the supply of talent in the market, the outsourcing cost where available talent is limited, and the increasing talent acquisition cost.
Talent management teams are using reskilling to train individuals with job roles disrupted by technology. 50% of the jobs are liable to be disrupted due to automation and new digital technologies. Draup’s analysis found that Automation Test Engineers can be given the necessary skills to a Computer Vision Engineer in about 8 – 12 months.
Draup’s comprehensive analysis of the Computer Vision role brought to light the present and future scenario of the role. The report informs about the demand for the role across industries, global hotspots for the role, and how HR can align their talent management strategies towards reskilling.
Draup is an AI-driven reskilling and talent intelligence platform with insights into the talent market. Its dashboard provides talent management teams access to the entire talent ecosystem, understand the situation, and plan to utilize the insights to benefit the organization.