Home
/
Blog
/
Hiring Tools
/
How AI Is Transforming The Talent Acquisition Process In Tech?

How AI Is Transforming The Talent Acquisition Process In Tech?

Author
Nidhi Kala
Calendar Icon
November 29, 2022
Timer Icon
7 min read
Share

Isabella is a recruiter at a multinational company with 1000+ employees. Her responsibility is to recruit the top talent—which she performs well by consistently looking for candidates that fit the company culture, collecting their information, reaching out to them, screening them, and finally interviewing them.

But…

She carries out the entire process manually—leading her to invest much time in the manual tasks—resulting in disengaged candidates (because who likes to wait this long?).

If you are a recruiter just like Isabella, you’ll soon find yourself burnt out and left without achieving your recruitment goals. This is where artificial intelligence has got you covered. In 2023, AI will play a major role in shaping candidate recruitment and making the lives of recruiters easy.

In this article, we’ll help you understand how artificial intelligence is used in technical recruitment.

What is artificial intelligence?

According to Techopedia, artificial intelligence is the field of study in which computerized systems can learn, solve problems and autonomously achieve goals under varying conditions. Simply put, artificial intelligence is about training the computer or the bot to do tasks that humans do—by feeding more data.

So what does artificial intelligence in technical recruitment refer to? It means a relevant technology has been used in the hiring process. There are three basic tech models artificial intelligence uses: descriptive, predictive, and prescriptive.

  • Descriptive: It tells us about what’s happening
  • Predictive: It gives us an image of the future
  • Prescriptive: It tells us what we should do based on the insights and findings

With this technology in the recruitment software, HR teams can focus on more strategic tasks without burning themselves out with manual efforts like candidate sourcing and outreach campaigns. Because organizations want to accelerate their talent acquisition efforts at scale, they hire recruitment agencies to find the top tech talent. But, when AI recruitment software is employed at your organization, you won’t have to rely on these recruitment agencies entirely—saving up 15-25% of employees’ first-year salary.

How can AI be used in technical recruitment?

Here are 7 ways how artificial intelligence helps improve the technical recruitment processes and set the ongoing recruiting for success.

Candidate sourcing

Recruiters spend ample time identifying top talent across hundreds of sources and platforms to find the best possible matches based on the job description—from job portals to social media profiles, they source candidates from multiple channels which is a time-consuming process.

When a recruiter sources tech candidates via LinkedIn, here’s what their process looks like:

  • Searches for the candidate on the LinkedIn search bar with relevant keywords
  • Gets a list of candidates from the received results
  • Scans the LinkedIn profiles of candidates who match the job requirements
  • Reaches out to them via Inmail or LinkedIn DMs
Improved technical recruitment with recruitment software

Recruiters have to invest time searching and scanning each profile. The process is toilsome. On average, recruiters lose 14 hours per week completing such tasks manually. Clearly, using recruitment software tools that help with candidate sourcing is a much better option. These tools look for candidates matching the job requirements and conduct outreach campaigns.

💡Pro tip: Use tools like Fetcher and Recruitee

Candidate screening

In a traditional screening process, recruiters prepare and send assessments to candidates. Once the candidate submitted the assessment for review, the recruiter checked each assignment manually.

Imagine the time a recruiter has to invest if they were to screen 100 such assessments manually. If the recruiter screened 100 candidates, they had to review all the dedicated assignments.

With AI, role-based assessments chop recruiters’ time to half. Role-specific assessments are the way AI screens the candidates. Candidates take these tests to showcase their knowledge and skills.

HackerEarth’s Assessments help you evaluate the developers’ skills with advanced coding assessments—all you have to do is upload job descriptions, create coding test interviews and find the top developers with the auto-generated leaderboard. Also, you can create the coding test interview from the list of interview questions to simplify your process.

For example, Nirvana Solutions used HackerEarth’s Assessments for technical candidate screening and reduced their cost per hire by 25%.

Why this works: Before using the automated screening method, the company relied on manual screening of applications which was a time-consuming process and led the company to exhaust its resources. Now, Nirvana’s team takes 5 minutes to create automated assessments and around 30 minutes to create the assessment from scratch. After the candidates submit the test, recruiters analyze candidate reports for each candidate’s performance—helping them screen and identify the top talent within 10 minutes.

Posting jobs

If you google “job boards to attract diverse candidates”, you’ll find endless results. Earlier, the most common were Naukri.com and Indeed. But, with time, job boards have expanded allowing organizations to distribute their job listings and attract the right tech talent.

Unfortunately, mass distribution of your job listing isn’t possible when you are a one-person army. That’s where AI tools like GoHire help recruiters and organizations automate job posting across different job boards.

Also read: A Checklist For Writing Job Postings That Actually Work

Hiring remote workers

Gone are the days when companies hired candidates from the same location. Walk-in interviews have been replaced by video interviews—which has helped companies to hire candidates while sitting in the comfort of their homes.

Sidenote: If your company follows a hybrid or remote work model, AI amplifies your recruitment efforts.

For example, after screening the candidate with automated assessments, you move to the next stage of conducting a face-to-face interview—which is conducted using a video platform like Zoom and Google Meet.

These tools miss out on features that recruitment software possesses, making AI in recruitment more prominent.

Let’s say, after screening the candidates via HackerEarth’s Assessments, you can use FaceCode to invite the candidates to schedule interviews, conduct live coding interviews with a panel of 5 interviewers, and evaluate them based on automated interview summaries with AI-based behavioral insights.

Diversity hiring

Unconscious bias is one of the staggering norms in the tech industry. Because tech is a male-dominated industry, companies see them as the right candidate compared to women. A 2022 report by Celential.ai emphasizes the gender diversity of software engineers where women represent only 21% of the workforce in software engineering.

And this bias starts right at the beginning of the technical recruitment process. Recruiters analyze the candidates based on multiple factors like location, gender, and educational qualifications on reference by the company’s employees.

But when AI comes into play in the recruitment industry, you shift towards unbiased hiring. This Twitter thread by Diversity Council Australia shares how women feel confident about applying to tech roles when companies use AI recruitment software.

Diversity hiring helps in technical recruitment

Image Source

There are two ways in which AI recruitment software removes biases:

  • When you use AI to send assessments to the candidates, you test them based on their skills. Once they submit the assessment, you get a report of each candidate and how they performed—this analysis helps you screen them and move to the next stage. During this process, your recruiter won’t focus on the candidate’s resume, location, or gender. They’ll *only* focus on the candidate’s skills and how they performed the assessment reducing biases.
  • When interviewing candidates, robots can conduct face-to-face interviews. They analyze the candidate’s response based on the conversation, facial expressions, and a mix of natural language processing and body language analysis—which helps remove prejudice

Data collection

Imagine adding recruitment software that sorts data and saves the 5 hours that you’d have spent doing the work manually. You could focus on improvising your current recruitment strategy, in these 5 hours.In a traditional setup, a recruiter would start by writing the job listing and sifting through inbound job applications to find the right fit.In the modern setup, recruiters find candidates on LinkedIn to build the database. Before building this database, they have to brainstorm and answer questions like:
  • How can I find the ideal candidate on LinkedIn?
  • How to identify and shortlist the candidate for a specific yet complex set of skills?
Once you identify the answers to these questions, you start your search, collect data and build the candidate’s database to carry out the recruitment process further. But here’s the thing: sorting through multiple data points and collecting candidates’ data is a tedious process. Employing recruitment software that collects candidates' data and analyzes it, candidate sourcing becomes much easier.AI Recruitment software like Hiretual acts as a candidate data engine for your tech recruiting requirements—it centralizes all your talent management and helps you source across 750M+ profiles and actively rediscovers lost profiles.

Candidate onboarding

From sharing the resource documents based on their role to telling them about the salary processing, health insurance, and so on—you need to exchange a lot of information with the new hire once you onboard them.No doubt you can rely on a single person assigned for helping in the onboarding process, but the time taken to onboard the new hire will take more time than expected impacting the credibility of the organization.With AI employed to onboard new employees, you can simplify the overall technical recruitment process.Seamless onboarding of new hires with chatbotFor instance, Unilever implemented artificial intelligence to onboard new employees. The company used Unabot, a natural language processing (NLP) bot to understand what employees need to know and fetch the information for them when asked. The AI acts as a forefront for questions employees have—from HR questions to department-specific questions like IT systems and allowances. Beyond this, it also answers questions like:
  • Where is the parking available?
  • What’s the timing for shuttle buses?
  • When are the annual salary reviews due to take place?
Basically, Unabot is the Alexa for Unilever—helping the company get rid of the back-and-forth of email and Slack messages to find and send resources when required.
Also read: Remote Hiring and Onboarding Tips for Technical Roles

Get ready to transform your technical recruitment process with artificial intelligence...

No doubt why recruiters like Isabella constantly ask themselves, “how to recruit tech talent?”. When you carry out each recruitment task manually, you are compressing the space for productivity and efficiency. A simple way to get out of this situation is by employing an AI-based recruitment software in your organization that:
  • Increases pay transparency for employees
  • Enhances candidate experience with pre and post-hiring communication
  • Provides accurate assessments of diverse candidates and identifies the most qualified candidate

Subscribe to The HackerEarth Blog

Get expert tips, hacks, and how-tos from the world of tech recruiting to stay on top of your hiring!

Author
Nidhi Kala
Calendar Icon
November 29, 2022
Timer Icon
7 min read
Share

Hire top tech talent with our recruitment platform

Access Free Demo
Related reads

Discover more articles

Gain insights to optimize your developer recruitment process.

Vibe Coding: Shaping the Future of Software

A New Era of CodeVibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today,...

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.Systems Design, in its...

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers. The ease and...

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
Top Products

Explore HackerEarth’s top products for Hiring & Innovation

Discover powerful tools designed to streamline hiring, assess talent efficiently, and run seamless hackathons. Explore HackerEarth’s top products that help businesses innovate and grow.
Frame
Hackathons
Engage global developers through innovation
Arrow
Frame 2
Assessments
AI-driven advanced coding assessments
Arrow
Frame 3
FaceCode
Real-time code editor for effective coding interviews
Arrow
Frame 4
L & D
Tailored learning paths for continuous assessments
Arrow
Get A Free Demo