How AI Is Transforming Software Engineer Hiring

Artificial intelligence has generated a lot of buzz lately. Popular AI techniques like computer vision and object recognition have revolutionized the scope of working across healthcare, science, retail, and education to improve the accuracy of success.

More than just a supercomputer generation, AI recreated human capabilities in machines. Nowadays, AI-powered software is used to automate the daily set of business operations and ease product hassles of departmental stakeholders. One of the departments for which AI works wonders is human resources (HR).

Many recruiters and HR professionals have decided to transcend into an AI mechanism for better optimization of business assets and growth. In practice, some have already integrated artificial intelligence software with their existing tech stack and employed a better-qualified workforce without stretching their budget or time.

Hiring a qualified candidate for any important role demands cross-communication and the correct exchange of information. Invest in a viable talent intelligence software that can help strategize your candidate sourcing for maximum engagement and conversions.

How does AI-based recruitment solve hiring challenges?

The entire office suffers when talented employees leave. Never mind the cost of hiring, onboarding, and upskilling a new member. Having a talented coworker leave increases the risk of employee attrition and a questionable reputation.

As the great resignation continues, many companies are turning to AI-driven HR software to increase retention rates and reduce costs. Looking beyond the conventional HR practices and managing every part of the software engineer lifecycle is a key to increasing talent acquisition margin.

Many modern and secure AI recruitment solutions easily connect the dots between companies and suitable candidates for particular job roles. In some cases, Data-driven recruiting and HR analytics use tangible company analysis and skills insights to solve recurring recruitment challenges and create high-quality talent pipelines.

All in all, AI fights employee turnovers for the better and optimizes recruitments to increase employment opportunities.

Also read: Common virtual recruiting pitfalls and how to avoid them

Components of an AI recruitment strategy for software engineers

How can AI-based recruitment Streamline the Talent Acquisition Process?

Hiring activities of a company are mainly outsourced to third-party AI recruitment agencies that run machine learning-based algorithmic expressions on candidate profiles. ML algorithms are pre-trained on massive datasets that increase the candidate selection efficiency and reduce the endless sifting and browsing of resumes.

Let’s look at how AI skills up the candidate game of a particular company in detail :

Pre-screening chatbots: AI conversational chatbots can bridge the gap between the recruiter and the applicant. It replaces the “recruiter phone screen” stage in the hiring process and screens candidates with a human touch. Built with evolving NLP technology, it screens a candidate’s ability to fit into their contesting roles by recognizing their pattern of communication.
Resume parser: The resume parser scans candidate resumes and social media profiles to analyze their experience and education. It generates specific codes and parses information according to the organization’s competitive shortlisting guidelines. It also categorizes a few samples for further processing and internally sorts them to produce the required output.
Filtering: The resume samples are further filtered based on custom criteria of age, background, qualification, etc., and forwarded to the concerned team. This candidate data can be stored in the cloud using reliant blockchain technology. The samples are clustered together to obtain a small set of profiles submitted for final evaluation.
Automated panel assignment: As the profiles finalize, an automated evaluator panel is assigned to individual candidates for further screening. Softwares such as Greenhouse recruiting and Skillate help connect evaluators with candidates for additional rounds of interviews. Such cloud-based solutions aid in faster execution of interview rounds and declaration of results while saving up time and effort.
Automated candidate assessment: Candidates usually appear for a pre-assembled cognitive aptitude test to check their cross-skill ability. Now companies are preparing these tests using AI writing tools that curate extremely logical questions to test candidates’ reasoning ability.
Automated candidate scoring: After the test, the machine learning algorithm analyzes the correctness of every answer and populates an unbiased and trustworthy score. It recommends the score to the assigned panel, who then amends it to a normalized score.
Personalized campaigns: Given the success of candidate evaluation, the system rolls out offer letters to selected software engineers with a note of personalization to make them feel connected and satisfied with their achieved milestone.
Dashboard and analytics: Advanced reports track every stage of the recruitment funnel and provide data-driven metrics regarding hiring campaigns to optimize future outreaches.

Investment funding to build better AI recruitment solutions

The potential of AI recruitment has been witnessed by different stakeholders and investors around the globe. A considerable percentage of the workforce operating in large software industries has witnessed this transition successfully and opened doors for new funding opportunities.

B2B SaaS companies like XOPA, a Singapore-based AI recruitment platform, announced $4.2 million Series A funding led by ICCP SBI Venture Partners. Phenom, another global HR technology company, raised over 100 M in series D funding, giving it a valuation of over 1 billion! This drastic revolution has impacted the decision-making of top investors and funders for better ROIs.

Meeting the tight budgets of a particular organization and easing the hiring stress on the HR professionals has made artificial intelligence more portable for employment.

Challenges of using AI in recruitment

Ethical concerns and bias

One of the most significant challenges in adopting AI for recruitment is the potential for bias and ethical concerns. AI algorithms rely on historical data, which can contain biases related to race, gender, or socioeconomic factors. If not carefully managed, AI systems can perpetuate these biases, leading to unfair and discriminatory hiring practices. It’s crucial for organizations to implement strict ethical guidelines and continuously monitor AI systems to mitigate these risks.

Data privacy and security

AI in recruitment relies heavily on collecting and processing vast amounts of candidate data, including personal information. Ensuring the privacy and security of this data is paramount. Organizations must comply with data protection regulations and invest in robust cybersecurity measures to prevent data breaches and protect candidates’ sensitive information.

Skill gap and training

Implementing AI in recruitment requires the upskilling of HR professionals and recruiters. Many organizations face challenges in providing adequate training to their staff to effectively use AI tools and technologies. Bridging the skill gap and ensuring that the recruitment team is proficient in utilizing AI systems is essential for successful implementation.

Transparency and explainability

AI algorithms can sometimes be seen as “black boxes” where decisions are made without clear explanations. Lack of transparency and explainability in AI-driven recruitment processes can lead to distrust among candidates and employees. To address this challenge, organizations need to ensure that their AI systems are transparent, and decisions can be explained to candidates and stakeholders.

Finding the balance between automation and human insight

Finding the right balance between AI-driven automation and human judgment can be challenging. While AI can streamline and enhance many aspects of recruitment, it should complement, not replace, the expertise of human recruiters. Ensuring that AI augments human decision-making rather than substituting it is a key challenge for organizations embracing AI in recruitment.

By addressing these challenges effectively, organizations can harness the benefits of AI in recruitment while maintaining ethical standards, data security, and the human touch necessary for successful talent acquisition.

The promise of a better-engineered workforce

AI is a cue card to a tech-optimized future of recruitment. It is the ultimate go-to that can help build an employer’s brand faster and recruit a skilled workforce.

Not just recruitment, AI can seamlessly automate, manage and scale your workforce planning and payroll management to diversify business outcomes and set a new trajectory for sustainable growth. It can be your strength to navigate through the complexities of the new world order and ignite a light of empathy in an otherwise computerized recruitment process.

Discover more articles

Gain insights to optimize your developer recruitment process.

Hackathons

Vibe Coding: Shaping the Future of Software

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.

Tech Assessment

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 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.

AI Recruiting

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 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

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
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
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
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.

Role of AI In Hiring Software Engineers