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Why 80% of India's engineers remain unemployable in the software sector

Why 80% of India's engineers remain unemployable in the software sector

Author
Raghu Mohan
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December 4, 2016
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3 min read
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It's common knowledge that India has a lot of educated people. That’s wonderful. But the lacuna casts a pall over the mood.

Reality bites, rarely pleasant…You might agree with me if you read the National Employability Report by Aspiring Minds, an employability evaluation and certification company, released earlier this year. The New-Delhi based company’s research study tracks more than 150,000 engineers who graduated in 2015 from over 650 colleges in India.

Did you know?

  • Only 18.43% of the engineers are employable in the software sector
  • Only 3.84% engineers suited to tech roles in startups
  • Nearly 27% of engineers failed to even snag an interview

Most numbers in the report are grim, from an employability percentage of 3.67% of Software Engineers for IT product companies to 17.91% of Software Engineers for IT service companies. Design engineers are not too lucky either with electronic engineers being the most employable at 7.07%.

Finding these abysmal statistics thought-provoking, we had a rather animated discussion at work about the “whys” and the “hows” and have come up with a few key observations:

Outdated learning and exam culture:

outdated classroom teaching

Whatever the reasons might be for the poor show, I believe it is sad that India's best universities are nowhere in the Top 100 in the world. The best India could do as of now: The Indian Institute of Science (IISC) in Bengaluru is at 152 and the Indian Institute of Technology (IIT)-Delhi is at 185 in the QS World University Rankings.

Indian curriculum is behind times as far programming languages are concerned. They stick with BASIC, FORTRAN, and some “marked for death” like PERL, Flash, Algol, and Object Pascal; how are these students expected to make headway into a world of Java, C, C++, Python, Ruby on Rails, etc.? (Those fortunate enough to go to some Tier I institutions do reap benefits of excellent professors and course design.)

The situation is worse in the case of core engineering such as mechanical or civil profiles. Like Aspiring Minds CTO, Varun Aggarwal, said, "The science of manufacturing has moved way ahead but we continue to teach outdated concepts to students. For India to become the world's manufacturing hub, we need to lead from the front in our understanding of cutting edge methods, knowledge-driven management and implementation capability."

Exams still force the students to memorize by rote ancient textbooks, with no comprehension of the basic concepts. It is no surprise then if they don’t bring the Nobel home, right? Most Indian children are expected to spend hours in “coaching” classes to get into engineering or medical colleges. Somehow many manage to, merit, money, no one really cares. With no passion to learn, to apply, to create, these engineers are only interested in finishing their 4-or 5-year degrees. What happens after is something else altogether.

Theory vs. Practice

theory vs practice

“It’s the learning ability. It’s the ability to process on the fly. It’s the ability to pull together disparate bits of information.” This what Laszlo Bock, Senior Vice President of People Operations at Google, Inc. told the New York Times in an interview in 2015. The tech giant apparently doesn’t care much about GPAs. Analytical and logical skills, please.

Most Indian engineering graduates, be it IT or Electronics engineers, fail when they are expected to apply basic principles to solve real-world problems. With neither the requisite analytical skills nor a commendable command of the domain, they flounder. They need “specific” training. That’s an expense that not everyone in the industry wants to incur. Universities need to bridge this gap and soon. For instance, they can encourage participation in coding challenges that companies like HackerEarth, SPOJ, and CodeChef conduct and introduce IT engineering students to competitive programming or hackathons.

Even companies like Wipro, TCS, and Infosys are committed to re-skilling or up-skilling their people—they promise to pay you more if you learn newer technologies. For example, with applications being moved to cloud computing, the engineers would need to know Go. For self-learners, the options are aplenty with premier e-learning providers like Udacity and Simplilearn offering you what the market demands. All of this sounds easy, but it is not—quite capital and labor-intensive.

Poor language skills

poor communication skills

And by language, I mean English. Effective communication is key to succeeding in the corporate world. This is not to put down our Indian language mosaic any way. We have come a long from the popular BBC sitcom Mind your English in the 70s, but going by this video, I’d say we have miles to go.

According to the Aspiring Minds’ National Spoken English Skills Report (SES), 52% of Indian engineers can’t get jobs because their spoken English skills are nothing to write home about. Fluency, sentence construction, pronunciation, and basic grammar would seem to be alien skills to some. Obviously, watching reruns of “Friends” and listening to Taylor Swift don’t seem to be working.

In the software sector, especially, engineers interact with an English-speaking workforce spread across the world. Cross-cultural team communication and client-handling skills are not taught in our colleges, unfortunately. Although an engineer could be brilliant, the inability to put forward his views effectively could well cost him his chance. There is much to be said in favor of behavioral, personality development, and people management skills helping engineers land their dream jobs. We need to reinforce these additional competencies as key elements of continuous learning.

I am sure you can think of so many more reasons why our engineering graduates are feeling the pinch of rising unemployment more than ever. These problems have been around for a while now and if they still haven’t changed, I don’t expect them to change either. Well, not to be predicting doom, but they won’t change fast. People need to think beyond just getting a job. While learning for learning’s sake and doing the job that you love to do is utopia; the first step toward it would be to find a middle ground between the ideal and reality. Keep jobs as a priority, but make people attain different goals to achieve it. Put out industry relevant problems and a job opportunity for everyone who can solve the problem within constraints. Not only is this industry relevant, it also lays emphasis on the importance of learning the basics, as the stronger your foundations, the quicker and better you can solve these programs. Apart from redesigning curricula and getting competent tutors, students need awareness and exposure via industry interaction.

Like they say, it is insanity to keep doing the same thing again and again and expecting a different result. Understanding is the first step, resolving the next.

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Raghu Mohan
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December 4, 2016
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3 min read
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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.

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.

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


  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.
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