Blockchain developer – how to hire the right professional

Maciej Zieliński

16 May 2021
Blockchain developer – how to hire the right professional

There are hundreds of new projects using blockchain technology every month around the world. It is estimated that by 2025, the value of the blockchain market will reach 39.5 billion. Therefore, blockchain remains one of the few industries where the demand for specialists so far exceeds the supply. In that case, which blockchain developer will meet your company's expectations and how to find one?

How much demand is there for Blockchain developers?

Entrepreneurs around the world are investing more and more money in blockchain development, and companies like JP Morgan, Oracle, Amazon, and Facebook are already using blockchain technology to handle their daily financial operations. 

If the number of top companies, including the world's largest banks and real estate giants, interested in blockchain technology is growing every day, there is basically no doubt that the demand for Blockchain developers is also growing. 

That's why LinkedIn has already rated blockchain as the most needed skill in 2020. And the U.S. Bureau of Labor Statistic has ranked Blockchain programmers among the fastest growing occupations in 2019-2020.

What skills a blockchain developer should have?

We have already written more about the skills needed by a Blockchain programmer in our article Blockchain programmer - how to become one?  

In a nutshell, every Blockchain programmer should understand the concept and tenets of Blockchain technology, cryptography, and be comfortable with topics such as tokens, smart contracts, decentralized applications and registries, and be fluent in the necessary programming languages. 

What programming languages should a blockchain programmer know?

The blockchain programmer you hire for your company should be proficient in all or most of the following programming languages:

  • C++
  • Scala
  • Python
  • Java
  • Javascript
  • Golang
  • Solidity

In addition, you should look for a blockchain developer who:

  • They previously worked with OOPS (Object-Oriented Programming Structure), this structure allows the program to be quickly updated when needed.
  • Deep understanding of language syntax makes a really big difference with Dapps
  • Be able to implement multithreading 
blockchain developer

Specific skills a Blockchain developer should have

A Blockchain developer should have a deep understanding of Blockchain technology and its applications. As a rule of thumb, a Blockchain programmer will be comfortable with the following concepts, which you should also be familiar with before interviewing him:

SHA (Security Hash Algorithm) - its familiarity is an absolute must, make sure that your company's Blockchain developer candidate knows the difference between SHA-1 and SHA-2 and can choose the right one for your application.

Encryption and decryption methods - necessarily along with a deep understanding of public and private key concepts. 

DLT (Distributed Ledger Technology) - a technology that allows data to be stored in multiple, distributed nodes rather than in a single location. The developer should be able to identify and explain its use cases in different Blockchain. 

Consensus- defines how new data is added to the blockchain (each node in the network must "agree" to add new information). A blockchain developer should distinguish between consensus methods such as PoS (Proof of Stake) and PoW (Proof of Work).

Tokenization - Tokenization is the process of converting both physical and non-physical assets into digital tokens using Blockchain technology. Every Blockchain developer should be able to create tokens and distinguish their basic types such as Utility, Security or NFT tokens. You can read more about them in our article: New token types – everything you need to know about them.

Dapps - decentralized, peer-to-peer applications.

Smart contracts - this is a self-enforcing contract in which the terms agreed between the parties are written directly into the lines of code. Smart contracts are the backbone of decentralized applications, where they allow for the abandonment of intermediaries. 

Various Blockchain Protocols - Currently, the most popular are Ethereum, Hyper Ledger Fabric and Corda. Blockchain projects mostly use one of these, so a Blockchain developer does not need to be proficient with all of them. On the other hand, his or her experience with them should certainly coincide with your needs. If you do not know which Blockchain protocol will work best for your project, our consultants can help you free of charge: contact@nextrope.com

Public and Private Blockchain - A blockchain developer should understand and be able to explain the differences between the two and justify their different business uses.

blockchain developer

Where to find a Blockchain developer

Online forums

Online platforms, forums, and groups on sites like Facebook, Reddit, and the IEEE Blockchain Community can also prove to be good places to look for Blockchain developers

Conferences

Nowadays, all such events have moved online, making it difficult but not impossible to find a blockchain developer at them. 

Schools

More and more universities are introducing courses focused on Blockchain. It is among their students and graduates that you will most easily find young people hungry for development and experience for the position of Blockchain programmer. 

Websites for freelancers

Among them, especially noteworthy are: CodementorXX-TeamToptal, BountyOne and Stack Overflow.

How to hire a Blockchain developer with Nextrope

We are a Polish software house providing Blockchain outsourcing services. We approach each project with great attention to detail and personal commitment. Among our clients there are revolutionary startups from financial and legal sector as well as one of the biggest banks in Poland - Alior Bank. You can read more about our success here. 

After years of work, we simply know the basics of fantastic projects. Want to find out why Nextrope - make an appointment for a free consultation: contact@nextrope.com

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AI in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series

AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!