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Reshaping The Education Landscape For India’s Future-Ready Tech Talent

Reshaping The Education Landscape For India’s Future-Ready Tech Talent

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Vishwastam Shukla
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October 11, 2021
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3 min read
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In May this year the global banking giant Credit Suisse reported that it would hire more than a thousand developers from India, making our country a base for global innovation, and home to over 25% of its tech workforce. I recount this statistic to underscore how over the last two decades, India has emerged as the largest pool of tech talent in the world.

The developer community grows exponentially year on year, and from playing a supporting role for other industries, we have now graduated to the forefront of developing cutting-edge technologies and innovative applications that will power the future. To sustain this momentum, we must understand the secret of creating a future-ready tech community lies in how we support and educate them.

With the continuous evolution of technology, devising education curricula that keep up with advancements becomes imperative.

When I was in college, we used hard disks and CDs for storing code. The first thing I learnt during my internship was how to store code in a code base and the importance of checking in to a central repository. I hadn’t heard of GitHub till then.This is but a small example, to showcase the gap between what coders are taught in professional institutions and what their work environment demands of them. In today’s startup culture where the learning curve is extremely steep and coders are expected to be shipping code as soon as they join work, rote learning has no reason to exist at all.
I know from experience - my own, and that of coders I have worked with - that what we are taught in our four-year degree courses leaves us underprepared for the workplace. The emphasis then should be on a holistic curriculum that prioritizes conceptual understanding and practical applications, over theoretical knowledge.

Let’s take another example - that of a data scientist, since this is a role currently in demand. An undergraduate student studies a plethora of courses in their four years, and dedicates only a semester or so studying core AI and Data Science topics. At work, they are expected to ace complex business problems with just a few months’ of knowledge. Needless to say, it is tough.

Many data scientists end up using online resources, certification programs, and practice platforms like Kaggle to upskill themselves on their own. This conclusively proves that the content of current engineering curriculums is nowhere it needs to be.

Skill-focused education that prioritizes and encourages interest in new and early technologies should be the norm going forward.

I believe there is ample scope to revamp our current college curricula and focus on improving students’ skill sets. One way of achieving this could be to condense our four-year courses. Colleges can then:
  • Create a teaching framework that includes at least 1-2 core languages like C++ and Python
  • Include the most important and popular coding frameworks for specific industry roles like backend or frontend engineers, or data scientists
  • Substantiate this with multiple internship programs and an ecosystem that hones the technical skills of students and fosters upskilling and growth by encouraging them to build practical applications
Reskilling is key to continuous, long-term growth in the developer world. Technology changes every day and a self-starter who can pick up new skills on the go is considered a prize in the business world. Our education system also needs to provide students with the means to be self-sufficient and job-ready. The need of the hour is not just learning new technical skills. Students must learn more about learning itself.

Along with this, there needs to be an acknowledgment of the soft skills a developer needs to excel at work.

It is always nice to have a high CGP, but the students who crack the top jobs have more than this. In the real world, a coder’s secondary and tertiary skills are their bread and butter. At school, it may not always be important to communicate well, but when you are with your peers and superiors, not being able to communicate well can make a huge impact on how you grow within the organization. I have personally seen many good coders stay stuck in their jobs because they couldn’t communicate their issues, and ask for help.
My mentor at my first job said to me that my emails were ‘pithy’. Even today, I reread my emails thrice before hitting send. Why? Because part of my job description involves distilling technical jargons into layman terms so that the other spokes of the business wheel (namely marketing, customer support, product management, etc.) can do their jobs as well.
I am glad someone pointed out my lack of communication skills to me early on and allowed me to improve. Students in engineering colleges need this particular brand of mentorship as well. Along with their core skills, they should be given opportunities to groom themselves. Colleges can work with companies and provide mentorship programs for young graduates to help them with interview prep, and other life skills so that our graduates leave school with a well-rounded skill set.

The pandemic has changed the way we look at education and learning. Colleges need to take note of this.

Online learning has created a level playing field irrespective of location and delivery channels. Education has been democratized and anyone with access to the internet can potentially learn the skills they need to be a coder. Compared to these online resources, the cost of a college degree remains comparatively high. (Sidebar: I took a full course online on Blockchain and Crypto by Gary Gensler, a professor at MIT, free of cost. Imagine what a specialized course like this would cost in a premier institution!)
As businesses move ahead with a skill-first approach to hiring, it won’t be long before college pedigree loses its sheen. You may hail from an IIT, but can you code better than the self-taught developer who learned their skills on YouTube? There are ISAs that offer a reputable degree at a fraction of the cost that a premier institution in India does. The gap is narrowing, and the high cost of a degree could be the final nail in the coffin for engineering colleges.
For educational institutions to retain their prominence, they must create a curriculum that meets the needs of the future. Collaboration between educators and tech companies is critical. Foresight and expertise should be the guiding principle for educating future tech talent. It is imperative to have education and training that have well-defined outcomes, allowing education providers to work backward from the market requirement and future advancement.

To wit...

There is a growing consensus that the demand and nature of our jobs have changed over the years and the importance of coding as a massive in-demand skill now cannot be ignored.The pandemic has eliminated barriers of geography that existed. This has opened up opportunities for skill-based learning that goes beyond large cities. Businesses now realize that education and work can happen anywhere - interest and ability are the only deciding factors. They are more than happy to hire developers with proven skills rather than with good resumes. Businesses need tech talent that can foresee challenges and remain forearmed. Education plays a key role in building this future-ready talent pool.

The aim of education and training, therefore, should be to create a new generation of coders who are not only great programmers but are also well-adapted to a changing world of remote work and rapid growth, which demands innovation even amid a crisis. Geography is no barrier to learning anymore, and skills are the only currency in this new normal. Our education systems must adapt, pronto.



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As originally published in The Hindu Business Line.

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Author
Vishwastam Shukla
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October 11, 2021
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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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