How Much Recruitment Automation Do You Need When Hiring a Dedicated Remote Development Team
What Makes DevOps Hiring So Different From Any other IT Project
To say that the hiring process is becoming harder and more complex would be an understatement at this point. Not only is there an ongoing talent war, the sheer amount of data that hiring managers have to sift through means they are more likely to make mistakes or even overlook promising candidates. These problems are only exacerbated when hiring a dedicated remote development team. Remote operations bring with them a number of unknown variables that makes accurate candidate selection even more difficult. Fortunately, advances in AI and machine learning are proving to be invaluable in recruitment. Many low and mid-level processes that took weeks before can be easily carried out with AI. But, how much automation can we realistically expect? Can all aspects of hiring be handed over to AI? If so, are the days of hiring managers numbered? Let’s find out.
How Does AI Work?
Before we get to how AI can help you hire remote DevOps engineers. , it’s important to understand what makes it so different from regular computer programming. All computer software is essentially a collection of static code and algorithms. They receive input and spit out an output based on fixed conditions built into the software. Since it’s impossible to predict every output demanded from a piece of code, its performance depends entirely on how similar the required output is to the software’s original design. This is why developers have to constantly update their software to meet new requirements and changes in hardware architecture. Artificial intelligence is very much like regular software save for one difference — it is capable of rewriting its base code without human intervention. At its core, AI is an iterative algorithm that parses through huge data sets, analyzes them, creates output, measures results, and optimizes itself to perform better the next time. Ad infinitum. AI doesn’t need a programmer updating its code and algorithm based on how well it’s doing (although it does help). In doing so, it can “learn” the best strategy by continuously seeking the most efficient solution. While a self-modifying algorithm has its uses, it’s nowhere near human intelligence either, giving it important, albeit limited utility.
AI’s Role in Recruitment From a process automation perspective, all roles can be broadly divided into two categories — those that consist of simple processes that don’t deviate much from their base conditions, and those with complex, open-ended processes that require creativity, foresight, and intuition. The hiring method is no different. Once a need is identified, job descriptions are drafted and posted to various sourcing portals. Applications that are received are then evaluated against the task requirements. The most promising candidates are interviewed and evaluated. The position is then offered to the best amongst them. From job identification to collecting and analyzing resumes will fall under the first category as the processes are fairly simple. Candidate interview and selection however, require a good deal of creative liberty and are therefore of the second variety. Of all the to-dos on a hiring manager’s list, It’s well established that screening resumes is by far the most mind-numbingly tedious task. On average, it takes a recruiter 23 hours to go through all the resumes for a single hire. The process involves numerous back and forth over email, sifting through mountains of PDFs, and meetings. So, there’s plenty that can go wrong. Pre-screening applicants are also the one thing that AI is best suited for. While the process can seem daunting, it can be automated with standardized forms for collecting resumes, coupled with Natural Language Processing (NLP) and machine learning to scrutinize each applicant without issues like observer bias, negligence, and oversight. The AI system can also provide highly targeted and relevant insights into the applicant pool’s overall capabilities and how well a recruitment drive is doing. Current attempts at using AI have proved promising so far and we can attest to its effectiveness. But, pre-screening candidates for interview and testing is also the extent of AI’s abilities in recruitment which can only make sense of skills and keywords, not people. Real hiring requires getting to know the person, their dreams, aspirations, personality, strengths, and weaknesses, which can only be done by another human being. When used properly, AI can help us automate low-level tasks with increased accuracy, drive down recruitment costs by freeing up man-hours, and provide actionable insights into each candidate for making better decisions. It is exceptionally useful for building remote software engineering teams because of the larger number of applications that online roles bring.
Let Us Help You Build Your Next Remote Engineering Team
We at Muoro know the challenges of remote working first hand. Our proprietary Engineering-as-a-Service model takes the best of what modern AI has to offer and pairs it with tested management and recruitment methods to create a workflow that helps us identify screen, and onboard highly qualified remote developers faster and more affordably than the industry average. Don’t take our word for it, with our free, 2-week trial, you can test our services with all its features. Feel free to contact us if you have any questions.