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In Conversation: Charles Rue, Head Of Talent Acquisition, IHS Markit

In Conversation: Charles Rue, Head Of Talent Acquisition, IHS Markit

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Kumari Trishya
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September 13, 2021
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3 min read
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Hire IQ by HackerEarth is a new initiative in which we speak with recruiters, talent acquisition managers, and hiring managers from across the globe, and ask them pertinent questions on the issues that ail the tech recruiting world. For this first edition, we spoke with Charles Rue, Head of Talent Acquisition (EMEA), at IHS Markit. Diversity and inclusion are topics close to Charles’ and his work is a reflection of his efforts to make the tech world a bigger better place for coders of all backgrounds. So, it was a given that the topic of choice for this conversation would be related to DE&I.

Read on!

HackerEarth: Please tell us a bit about yourself and your journey in the hiring world.

Charles: After a previous life in management consulting, I started my career in recruitment in Japan, which is a great training ground for a recruiter as it is a market where professionals tend to be loyal to their employer, and are therefore extremely difficult to dislodge, especially for roles at foreign firms. After heading the Financial Services practice there for nine years, I relocated to Hong Kong where I successively set up a new desk for an executive search firm, opened the local office for a global recruitment firm specialized in Financial Services, and finally joined the recruitment function of a large global bank via their RPO partner. There, I gained considerable experience in large scale, complex recruitment campaigns, in areas such as Retail and Corporate Banking, Asset Management, Insurance and the full spectrum of Digital Transformation.

This last experience gave me the opportunity to later on join IHS Markit, a world leader in critical information, analytics and solutions, and head their EMEA recruitment function.

HackerEarth: As a talent acquisition leader, when did you start to understand the importance of creating diverse teams? Are there any real-life examples you can share with us?

Charles: At an early stage in my career, I was aware that diverse teams can tackle challenges much more effectively due to the richness of perspectives, especially in complex, changing environments. The real battle was convincing my clients when I was working on the agency side because their candidate assessment methods were not robustly documented or consistent. Assessment bias was rife, and what was expected from external recruitment agencies was essentially reinforcement, where interviewers and decision makers with already developed opinions were selectively incorporating information that supported their own views. Later, when I was in-house, it was easier to influence stakeholders.

I recall a specific example where our recruitment teams focused on restoring the gender balance of a financial services sales team. During the following year, work environment indicators went up, positive client feedback was more numerous, collaboration increased, and revenue went up. That small-scale example helped develop awareness among the leadership team.

HackerEarth: IHS Markit has been in the industry for a long while. Could you shed some light on how the hiring policies have changed/evolved at the company vis-a-vis DE&I?

Charles: Openness has been at the center of IHS Markit company culture. While Diversity, Equity and Inclusion have underpinned our corporate strategy and the way we want to hire and develop our people, we certainly have developed a more structured approach in recent years. For example, we have enhanced our list of D&I partners to help us better understand and connect with under-represented candidate pools.

Our recruiting tool combines artificial intelligence and neuroscience to assist in removing unconscious bias during screening. On top of that, we have developed our own internal interviewing framework called the IHS Markit Way to help ensure consistent interview questions and that everyone is being assessed against the same unbiased criteria by a diverse panel. Finally, on the Early Careers front, we have added D&I organizations SEO London and Wall Street Bound as our main candidate sourcing partners during our 2020-2021 Intern and Graduate recruiting campaign.

Also Read: How To Increase Your Diversity Hiring ROI

HackerEarth: What do you think are the top 3 mistakes that companies new to diversity hiring make when formulating policies?

Charles: There are quite a few pitfalls when looking at improving diversity in the workforce. The first one is not getting genuine support from the top leadership team. That’s paramount. Hiring Managers will sense quickly if the company’s diversity goals are hollow or if there are real consequences for not supporting diversity in every hiring decision. Leaders must be 100% committed to the company’s diversity objectives, and keep communicating about their commitment internally and externally.

The second pitfall is missing the data. Diversity data is the very first step before a situation can be understood, and corresponding diversity goals can be set. Not collecting the right data, and compiling the data in effective dashboards is like shooting in the dark. It will frustrate teams and slow down adoption. A third pitfall is not asking help from diversity professionals. I think it is a common mistake as most HR and Recruitment functions tend to think that tweaking policies and buying assessment tools will single-handedly drive a more diverse workforce.

This approach is totally missing the cornerstone of an effective diversity strategy: diversity attraction, which can be translated into ‘how to transform a company to make it really inclusive?’, and ‘how to connect with underrepresented populations, and develop the right role proposition that will lead to an application?’ This is where specialized organizations can provide guidance on inclusiveness, and also leverage their extensive network within underrepresented populations.

HackerEarth: A question that we love asking everybody: Skills vs. Diversity – which one would you choose and why?

Charles: I genuinely don’t think we should have to make this choice. We should aim for both. If we can’t find both in a given market, companies should then go for diversity and then develop programs that will create skills internally. This is what we are doing at IHS Markit through our Early Careers recruitment programs.

We partner with specialized organizations and make sure our hiring outcomes fully support our diversity goals. Candidates for Internships and Graduate positions are assessed using consistent methods, against four role profiles. We select candidates who exhibit specific attributes and show growth potential. Our cohorts are nurtured so that required skills can be grown, while all the time we never had to negotiate on diversity.

HackerEarth: Have you come across D&I initiatives from various companies that have wowed you, and why do you think they work? (Examples can be AirBnB’s WeAccept campaign, or Salesforce’s equality groups).

Charles: BlackRock has created many positive D&I initiatives including the organization of their MOSAIC employee network, or the use of a Rare Contextual Recruitment System for early career recruitment in the United Kingdom. The latter recognizes that not every candidate’s achievements look the same on paper. Using the Rare Contextual Recruitment System allows BlackRock to see beyond an online application to better understand the circumstances in which each applicant’s achievements have been gained.

From BlackRock’s perspective, this process enables the firm to identify the best talent from all backgrounds. Deutsche Bank also has done interesting things in the area of gender diversity. Deutsche has won an award for its global sponsorship program ATLAS, which helps women progress to senior positions.

HackerEarth: Google, Facebook, Apple, Microsoft, and Twitter decided to start publishing an annual diversity hiring report in 2014. That was the first time that tech companies publicly acknowledged the diversity gap in their workplaces and vowed to change hiring practices.

Seven years later, there is only a marginal increase in diversity numbers at these companies. In your opinion, what are these companies:

  1. Doing well
  2. Doing wrong and how can they better it

Charles: Clearly the situation has not improved much. I’ve read recently that the proportion of US technical employees (coders, engineers, and data scientists) at some of these firms who are black or Latinx hasn’t risen since 2014. It seems however that the proportion of women has progressed, though no company is close to parity yet.

On the ‘plus’ side, all of these firms have made large investments into various education programs to encourage more women and minorities to consider tech, to help address a legacy of underrepresentation. On the ‘minus’ side however, all of these firms are growing, and are in need of much more under-represented candidates than they used to be, while attrition for these very same under-represented populations is clearly much higher than average.

Basically, despite all their investments, tech companies still haven’t addressed biases in their cultures, promotion criteria, and the broader issue of inclusion and belonging. These items will need to be on their agenda if they want to make an impact on their own D&I goals.

HackerEarth: There is a lot of talk about data-driven recruiting. When it comes to diversity hiring, what are the metrics you think talent acquisition managers should live or die by?

Charles: Purely from a talent acquisition perspective, there should really be three diversity metrics:

  • the first one measuring whether proportions of job applicants are reflective of the local population’s diversity mix (gender, ethnicity, socio-economic background, sexual orientation, etc.),
  • the second measuring whether the same diversity mix is eventually hired locally,
  • and the third measuring whether retention levels are consistent across populations, including women, minorities or under-represented ethnicities.
All three metrics should be measured at country level, across role levels, and department, so that data aggregation does not hide a local diversity issue. These three metrics will uncover attraction gaps and hiring/promotion bias, and should lead to a more accountable diversity strategy.

HackerEarth: Let’s end this with a tip (or two) for recruiters/talent acquisition managers who would like to amp up diversity hiring in their companies..

Charles: First, talk to your firm’s top leadership team and secure their commitment to taking responsibility for building an inclusive hiring process. Leaders should communicate their commitment to the principles of Diversity to the rest of the firm.

Second, work with your HR Analytics team and start measuring team diversity ratios before setting achievable targets.

Third, take concrete action by writing inclusive Job Adverts (the Gender Decoder tool is free!), advertising job adverts on diversity friendly job boards, actively reaching out on LinkedIn to underrepresented candidates, and by assessing candidates using objective and consistent methods.

Fourth, talk to professional D&I organizations that will help you refine and structure your approach. They have seen it all, and will help save a lot of time.

About Charles Rue:

Charles brings with him a decade and a half of recruitment experience at notable companies like HSBC, Eames

Charles Rue, IHS Markit

Consulting, and the Michael Page Group. He has been with IHS Markit since 2019 and is a champion of diversity and inclusion in the tech space.

Charles has more than 16 years of recruitment experience in the EMEA and APAC region, developing an expertise in volume (Experienced and Graduate) and senior to executive level permanent hiring in the Banking, Data, Digital, Insurance, Fintech, Asset Management and Payment Solutions sectors. Prior to joining IHS Markit, Charles was responsible for the delivery of large recruitment volumes for HSBC in Hong Kong.

Charles has been involved in a broad range of recruitment performance improvement projects and D&I initiatives in various setups, from external recruitment agencies, to RPO and in-house environments.

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Kumari Trishya
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September 13, 2021
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3 min read
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How I used VibeCode Arena platform to build code using AI and leant how to improve it

I Used AI to Build a "Simple Image Carousel" at VibeCodeArena. It Found 15+ Issues and Taught Me How to Fix Them.

My Learning Journey

I wanted to understand what separates working code from good code. So I used VibeCodeArena.ai to pick a problem statement where different LLMs produce code for the same prompt. Upon landing on the main page of VibeCodeArena, I could see different challenges. Since I was interested in an Image carousal application, I picked the challenge with the prompt "Make a simple image carousel that lets users click 'next' and 'previous' buttons to cycle through images."

Within seconds, I had code from multiple LLMs, including DeepSeek, Mistral, GPT, and Llama. Each code sample also had an objective evaluation score. I was pleasantly surprised to see so many solutions for the same problem. I picked gpt-oss-20b model from OpenAI. For this experiment, I wanted to focus on learning how to code better so either one of the LLMs could have worked. But VibeCodeArena can also be used to evaluate different LLMs to help make a decision about which model to use for what problem statement.

The model had produced a clean HTML, CSS, and JavaScript. The code looked professional. I could see the preview of the code by clicking on the render icon. It worked perfectly in my browser. The carousel was smooth, and the images loaded beautifully.

But was it actually good code?

I had no idea. That's when I decided to look at the evaluation metrics

What I Thought Was "Good Code"

A working image carousel with:

  • Clean, semantic HTML
  • Smooth CSS transitions
  • Keyboard navigation support
  • ARIA labels for accessibility
  • Error handling for failed images

It looked like something a senior developer would write. But I had questions:

Was it secure? Was it optimized? Would it scale? Were there better ways to structure it?

Without objective evaluation, I had no answers. So, I proceeded to look at the detailed evaluation metrics for this code

What VibeCodeArena's Evaluation Showed

The platform's objective evaluation revealed issues I never would have spotted:

Security Vulnerabilities (The Scary Ones)

No Content Security Policy (CSP): My carousel was wide open to XSS attacks. Anyone could inject malicious scripts through the image URLs or manipulate the DOM. VibeCodeArena flagged this immediately and recommended implementing CSP headers.

Missing Input Validation: The platform pointed out that while the code handles image errors, it doesn't validate or sanitize the image sources. A malicious actor could potentially exploit this.

Hardcoded Configuration: Image URLs and settings were hardcoded directly in the code. The platform recommended using environment variables instead - a best practice I completely overlooked.

SQL Injection Vulnerability Patterns: Even though this carousel doesn't use a database, the platform flagged coding patterns that could lead to SQL injection in similar contexts. This kind of forward-thinking analysis helps prevent copy-paste security disasters.

Performance Problems (The Silent Killers)

DOM Structure Depth (15 levels): VibeCodeArena measured my DOM at 15 levels deep. I had no idea. This creates unnecessary rendering overhead that would get worse as the carousel scales.

Expensive DOM Queries: The JavaScript was repeatedly querying the DOM without caching results. Under load, this would create performance bottlenecks I'd never notice in local testing.

Missing Performance Optimizations: The platform provided a checklist of optimizations I didn't even know existed:

  • No DNS-prefetch hints for external image domains
  • Missing width/height attributes causing layout shift
  • No preload directives for critical resources
  • Missing CSS containment properties
  • No will-change property for animated elements

Each of these seems minor, but together they compound into a poor user experience.

Code Quality Issues (The Technical Debt)

High Nesting Depth (4 levels): My JavaScript had logic nested 4 levels deep. VibeCodeArena flagged this as a maintainability concern and suggested flattening the logic.

Overly Specific CSS Selectors (depth: 9): My CSS had selectors 9 levels deep, making it brittle and hard to refactor. I thought I was being thorough; I was actually creating maintenance nightmares.

Code Duplication (7.9%): The platform detected nearly 8% code duplication across files. That's technical debt accumulating from day one.

Moderate Maintainability Index (67.5): While not terrible, the platform showed there's significant room for improvement in code maintainability.

Missing Best Practices (The Professional Touches)

The platform also flagged missing elements that separate hobby projects from professional code:

  • No 'use strict' directive in JavaScript
  • Missing package.json for dependency management
  • No test files
  • Missing README documentation
  • No .gitignore or version control setup
  • Could use functional array methods for cleaner code
  • Missing CSS animations for enhanced UX

The "Aha" Moment

Here's what hit me: I had no framework for evaluating code quality beyond "does it work?"

The carousel functioned. It was accessible. It had error handling. But I couldn't tell you if it was secure, optimized, or maintainable.

VibeCodeArena gave me that framework. It didn't just point out problems, it taught me what production-ready code looks like.

My New Workflow: The Learning Loop

This is when I discovered the real power of the platform. Here's my process now:

Step 1: Generate Code Using VibeCodeArena

I start with a prompt and let the AI generate the initial solution. This gives me a working baseline.

Step 2: Analyze Across Several Metrics

I can get comprehensive analysis across:

  • Security vulnerabilities
  • Performance/Efficiency issues
  • Performance optimization opportunities
  • Code Quality improvements

This is where I learn. Each issue includes explanation of why it matters and how to fix it.

Step 3: Click "Challenge" and Improve

Here's the game-changer: I click the "Challenge" button and start fixing the issues based on the suggestions. This turns passive reading into active learning.

Do I implement CSP headers correctly? Does flattening the nested logic actually improve readability? What happens when I add dns-prefetch hints?

I can even use AI to help improve my code. For this action, I can use from a list of several available models that don't need to be the same one that generated the code. This helps me to explore which models are good at what kind of tasks.

For my experiment, I decided to work on two suggestions provided by VibeCodeArena by preloading critical CSS/JS resources with <link rel="preload"> for faster rendering in index.html and by adding explicit width and height attributes to images to prevent layout shift in index.html. The code editor gave me change summary before I submitted by code for evaluation.

Step 4: Submit for Evaluation

After making improvements, I submit my code for evaluation. Now I see:

  • What actually improved (and by how much)
  • What new issues I might have introduced
  • Where I still have room to grow

Step 5: Hey, I Can Beat AI

My changes helped improve the performance metric of this simple code from 82% to 83% - Yay! But this was just one small change. I now believe that by acting upon multiple suggestions, I can easily improve the quality of the code that I write versus just relying on prompts.

Each improvement can move me up the leaderboard. I'm not just learning in isolation—I'm seeing how my solutions compare to other developers and AI models.

So, this is the loop: Generate → Analyze → Challenge → Improve → Measure → Repeat.

Every iteration makes me better at both evaluating AI code and writing better prompts.

What This Means for Learning to Code with AI

This experience taught me three critical lessons:

1. Working ≠ Good Code

AI models are incredible at generating code that functions. But "it works" tells you nothing about security, performance, or maintainability.

The gap between "functional" and "production-ready" is where real learning happens. VibeCodeArena makes that gap visible and teachable.

2. Improvement Requires Measurement

I used to iterate on code blindly: "This seems better... I think?"

Now I know exactly what improved. When I flatten nested logic, I see the maintainability index go up. When I add CSP headers, I see security scores improve. When I optimize selectors, I see performance gains.

Measurement transforms vague improvement into concrete progress.

3. Competition Accelerates Learning

The leaderboard changed everything for me. I'm not just trying to write "good enough" code—I'm trying to climb past other developers and even beat the AI models.

This competitive element keeps me pushing to learn one more optimization, fix one more issue, implement one more best practice.

How the Platform Helps Me Become A Better Programmer

VibeCodeArena isn't just an evaluation tool—it's a structured learning environment. Here's what makes it effective:

Immediate Feedback: I see issues the moment I submit code, not weeks later in code review.

Contextual Education: Each issue comes with explanation and guidance. I learn why something matters, not just that it's wrong.

Iterative Improvement: The "Challenge" button transforms evaluation into action. I learn by doing, not just reading.

Measurable Progress: I can track my improvement over time—both in code quality scores and leaderboard position.

Comparative Learning: Seeing how my solutions stack up against others shows me what's possible and motivates me to reach higher.

What I've Learned So Far

Through this iterative process, I've gained practical knowledge I never would have developed just reading documentation:

  • How to implement Content Security Policy correctly
  • Why DOM depth matters for rendering performance
  • What CSS containment does and when to use it
  • How to structure code for better maintainability
  • Which performance optimizations actually make a difference

Each "Challenge" cycle teaches me something new. And because I'm measuring the impact, I know what actually works.

The Bottom Line

AI coding tools are incredible for generating starting points. But they don't produce high quality code and can't teach you what good code looks like or how to improve it.

VibeCodeArena bridges that gap by providing:

✓ Objective analysis that shows you what's actually wrong
✓ Educational feedback that explains why it matters
✓ A "Challenge" system that turns learning into action
✓ Measurable improvement tracking so you know what works
✓ Competitive motivation through leaderboards

My "simple image carousel" taught me an important lesson: The real skill isn't generating code with AI. It's knowing how to evaluate it, improve it, and learn from the process.

The future of AI-assisted development isn't just about prompting better. It's about developing the judgment to make AI-generated code production-ready. That requires structured learning, objective feedback, and iterative improvement. And that's exactly what VibeCodeArena delivers.

Here is a link to the code for the image carousal I used for my learning journey

#AIcoding #WebDevelopment #CodeQuality #VibeCoding #SoftwareEngineering #LearningToCode

The Mobile Dev Hiring Landscape Just Changed

Revolutionizing Mobile Talent Hiring: The HackerEarth Advantage

The demand for mobile applications is exploding, but finding and verifying developers with proven, real-world skills is more difficult than ever. Traditional assessment methods often fall short, failing to replicate the complexities of modern mobile development.

Introducing a New Era in Mobile Assessment

At HackerEarth, we're closing this critical gap with two groundbreaking features, seamlessly integrated into our Full Stack IDE:

Article content

Now, assess mobile developers in their true native environment. Our enhanced Full Stack questions now offer full support for both Java and Kotlin, the core languages powering the Android ecosystem. This allows you to evaluate candidates on authentic, real-world app development skills, moving beyond theoretical knowledge to practical application.

Article content

Say goodbye to setup drama and tool-switching. Candidates can now build, test, and debug Android and React Native applications directly within the browser-based IDE. This seamless, in-browser experience provides a true-to-life evaluation, saving valuable time for both candidates and your hiring team.

Assess the Skills That Truly Matter

With native Android support, your assessments can now delve into a candidate's ability to write clean, efficient, and functional code in the languages professional developers use daily. Kotlin's rapid adoption makes proficiency in it a key indicator of a forward-thinking candidate ready for modern mobile development.

Breakup of Mobile development skills ~95% of mobile app dev happens through Java and Kotlin
This chart illustrates the importance of assessing proficiency in both modern (Kotlin) and established (Java) codebases.

Streamlining Your Assessment Workflow

The integrated mobile emulator fundamentally transforms the assessment process. By eliminating the friction of fragmented toolchains and complex local setups, we enable a faster, more effective evaluation and a superior candidate experience.

Old Fragmented Way vs. The New, Integrated Way
Visualize the stark difference: Our streamlined workflow removes technical hurdles, allowing candidates to focus purely on demonstrating their coding and problem-solving abilities.

Quantifiable Impact on Hiring Success

A seamless and authentic assessment environment isn't just a convenience, it's a powerful catalyst for efficiency and better hiring outcomes. By removing technical barriers, candidates can focus entirely on demonstrating their skills, leading to faster submissions and higher-quality signals for your recruiters and hiring managers.

A Better Experience for Everyone

Our new features are meticulously designed to benefit the entire hiring ecosystem:

For Recruiters & Hiring Managers:

  • Accurately assess real-world development skills.
  • Gain deeper insights into candidate proficiency.
  • Hire with greater confidence and speed.
  • Reduce candidate drop-off from technical friction.

For Candidates:

  • Enjoy a seamless, efficient assessment experience.
  • No need to switch between different tools or manage complex setups.
  • Focus purely on showcasing skills, not environment configurations.
  • Work in a powerful, professional-grade IDE.

Unlock a New Era of Mobile Talent Assessment

Stop guessing and start hiring the best mobile developers with confidence. Explore how HackerEarth can transform your tech recruiting.

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

Vibe Coding Difference

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 or Hostinger Horizons 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.

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