Hire OpenAI Developers

OpenAI Developer Demand Soars As the OpenAI API Unleashes New Capabilities

Hiring managers face a unique challenge when sourcing OpenAI developers: The classic “x years of experience” is moot. Why? Because the OpenAI API only became publicly available in 2020 and did not become widely used until 2022. Any hiring manager who copies and pastes a job description noting a rote “3 to 5 years of experience” requirement would be narrowing their candidate pool to near zero. While the right candidate must have some domain-specific experience, hiring managers have to lean harder on triangulating from adjacent skill sets than they would when hiring for a long-standing field such as Python development.

What’s more, soaring industry demand is leaving hiring managers competing against each other among a dearth of qualified candidates. Reuters reports that OpenAI generated $28 million in total revenue in 2022; by late 2023, it was bringing in closer to $80 million per month. That year saw the rise of ChatGPT, which was crowned as the fastest growing app of all time shortly after its release in January 2023 (a title it would hang onto until bested by Threads in July 2023). It now brings in nearly 200 million users each month. All kinds of businesses—tech companies or otherwise—are hungry to leverage AI now, and the pool of candidates is drying up quickly.

The demand for developers with strong command of AI capabilities has expanded across sectors, increasing the urgency of building a dedicated team that can adapt to fast-evolving tools.

This guide provides a solid foundation for navigating this competitive and rapidly changing landscape, including how to determine the skill requirements and experience level needed for your role. We provide actionable advice for writing an effective job description and conducting comprehensive technical interviews to make sure you find the best fit for your project.

What attributes distinguish quality OpenAI Developers from others?

Hiring a high-quality OpenAI developer is challenging because the right candidate needs to meet qualifications that extend beyond ordinary web development. Developing API solutions (especially at scale) is much more complex than creating static marketing websites, for example. Keep in mind that working with AI is different from working on AI: Even experienced developers building applications with OpenAI’s models are unlikely to understand the inner workings at a deep level because that knowledge is tangential at best to the work they do of integrating APIs.

Quality OpenAI developers also bring experience building ai-powered systems that streamline workflows and elevate overall user experience beyond basic API use.

A qualified candidate should display:

General API experience – A good OpenAI developer must have a solid foundation in application development and API integrations. APIs are what developers use to integrate software and third-party services directly rather than working in a user interface. API integrations are a cornerstone of software development and central to the process of working with OpenAI models. A strong candidate will have extensive experience integrating APIs for all kinds of use cases (such as payment processing, authentication, and manipulating user data), and this experience will carry over to building with OpenAI. Candidates must understand fundamental issues of security, performance, and stability, which are applicable to all API-related projects. This is not an easy skill set for hiring managers to validate; it’s a hiring challenge unto itself.

An understanding of how to work with AI – A strong OpenAI developer should be prepared to handle the unique quirks of artificial intelligence and generative AI (like ChatGPT’s proclivity to hallucinate or to be “lazy” and refuse to answer prompts). Nontechnical stakeholders often have vague ideas about how to use AI: “Let’s feed our data to it and see what happens.” The right candidate will be able to guide those conversations with a deep understanding of what kinds of tasks generative AI models are appropriate for, as well as the work involved in integration.

Specific experience with OpenAI models – The strongest candidates will have significant hands-on experience with some or all of OpenAI’s models:

Each of these products has unique nuances and different model versions with wide-ranging capabilities and costs. GPT, for instance, is available in several different versions, and the differences between them may be trivial in many use cases (e.g., straightforward questions and answers) but profound in others (e.g., complex reasoning). OpenAI updates its API and language models several times per year, and these updates can have a real impact on what’s possible for integrations. For example, GPT-3 and -4 are trained on data that was gathered up to September 2021; GPT-4 Turbo is trained on data gathered up to the end of 2023. If your use case requires access to information from 2022 and beyond, earlier versions of GPT will not suffice. The best candidates will be able to lead conversations on strategy and implementation based on their up-to-date knowledge of OpenAI’s models.

Complementary Skills for OpenAI Development

Because the OpenAI API can be integrated into various types of applications, any candidate who’s qualified for this role should possess a wide range of complementary skills, which might include:

Python – Python is one of the most common programming languages for building creative solutions with OpenAI. It has a large developer community and an official library for working with OpenAI’s software. Python developers interested in machine learning may have experience with specific tools like TensorFlow or PyTorch, but this skill set isn’t necessary to make productive use of the OpenAI API.

JavaScript – JavaScript is another commonly used programming language for building web and mobile apps with OpenAI. The company provides developers with a specially tailored library for working with the API in Node.js. Thanks to frameworks like Express.js and Next.js (both built on top of Node.js), developers can quickly assemble both internal and customer-facing AI tools that can scale over time. Experience with JavaScript and Node.js is great to see on any OpenAI developer’s résumé. In e-commerce settings, developers can leverage advanced AI to create personalized recommendation engines or chatbots that enhance customer experience and drive conversions.

Full-stack web development – Regardless of their programming language of choice, a competent API developer should be comfortable writing code across the full stack; that is, both back end (server and business logic) and front end (user interfaces). That said, the majority of candidates will likely be more comfortable on one side or the other. When hiring for an OpenAI developer, you’re better off looking for those with more back-end experience—a full-stack developer should be able to build a “good enough” user interface for internal tools, and if you’re building a customer-facing app, you’ll likely want to bring in specialist designers and front-end developers anyway.

Serverless and cloud architecture – “Serverless” is the term applied to apps that make use of cloud providers’ ability to offer compute power on demand, eliminating the need for developers to maintain traditional servers of their own. When done right, this approach can lead to massive savings in time and money. AWS, Azure, Google Cloud, and other providers offer a wide range of tools to aid in assembling serverless apps. Infrastructure is one of the most complex needs that must be addressed when building with OpenAI models, so this is a very valuable skill to look for in candidates.

Are advanced machine learning and data science skills required for OpenAI development?

Aside from research institutions and enterprise corporations, most organizations don’t need to bring on a formally trained machine learning or data science professional to make the most of the OpenAI API. High-quality API work demands an expert, but the developer who’s qualified for this role is an entirely different profile than, say, someone with a PhD in natural language processing (NLP), the field of research that’s given rise to OpenAI’s models. NLP as an applied skill is something that developers can pick up easily as they work with the API, and they don’t need a comprehensive understanding of the theoretical research behind it in order to make productive use of it. The same goes for topics like neural networks and deep learning, which are fundamental areas of study in AI but not necessary for an API developer to have experience with.

Though deep AI theory isn’t essential, leading developers can tap into cutting-edge, advanced AI APIs to create production-grade models without overcomplicating the development process.

Because the field of generative AI is so new (relatively speaking), many highly qualified candidates may only have limited domain-specific experience. However, it would be a mistake to discount candidates based solely on how much experience they have with the OpenAI API specifically. API skills are easily transferable, and if a candidate is knowledgeable about working with AI on top of them, they should be able to quickly gain proficiency with the OpenAI API even if they’ve never touched it before.

How can you identify the ideal OpenAI Developer for you?

Regardless of the industry or the project scope, it’s important for stakeholders to be informed about how AI models can be put to use to provide value to their clients or customers. Technological solutions are often poorly understood by those who don’t work directly in the problem domain. Sometimes AI systems can seem like pure magic; other times they can be frustratingly disobedient or hallucinatory. As you weigh the goals and aspirations for your project outcomes, be realistic about what’s possible with this novel technology.

If you have an existing product written in a given programming language, and you’re looking to fix or expand it, then it makes sense to narrow your search to candidates who work in that language. But for greenfield projects, hiring managers don’t need to focus on “skills gaps.” Try to avoid being biased about particular frameworks, languages, libraries, and plugins. Present your problem statement and let the candidate lead the discussion from there. Look for developers who can help with onboarding AI into your development methodology, facilitating smooth integration and adoption across your product lifecycle. Ideally, they’ll be able to recommend an optimal tech stack themselves. When given a blank canvas, the right candidate shouldn’t be intimidated by the choices necessary to launch an MVP (minimum viable product), which might include working in a programming language or framework that’s new to them.

Differentiating Between More and Less Experienced Candidates

API integrations are a fundamental part of full-stack web development, so you may get a fair amount of interest from junior developers for an OpenAI developer role. Less experienced candidates will be familiar with web development at a high level, but they may only have direct experience with one side of the stack—most likely back end if they’re seeking API work. They may need significant guidance to carry out complex and open-ended projects (such as implementing a customer service chatbot from end to end). They should be proficient in a programming language like Python, JavaScript, or C#, but it’s unlikely that they’ll have professional experience with more than one.

A mid-level developer will be able to discuss their battle-tested concepts for general API development, drawing from their years of experience before the OpenAI API existed. They should be professionally interested in artificial intelligence and able to guide conversations about how to solve problems for your business using AI. Consider it an added bonus if they have domain-specific experience using OpenAI models in production, even if it’s only in hobby projects that demonstrate their curiosity and their development chops. They should have mastered at least one programming language and some of its most popular libraries and frameworks, and they may be proficient in working with multiple languages across the full stack. Mid-level candidates often have proven experience with data analysis use cases, demonstrating how they’ve translated raw text or user activity into actionable product insights.

A senior-level developer who’s qualified for this role has probably been working with AI systems prior to the recent explosion in the field brought about by OpenAI. There’s a good chance they’ve already applied their experience in AI to a wide range of projects with several enterprise-level clients and have valuable insights to share about the process. They should be knowledgeable about all of OpenAI’s popular models and the differences between model versions. More experienced candidates can act as project managers, with strong communication skills, and may have prior experience as engineering leaders. They’ll be comfortable designing, building, and optimizing full-stack web apps and will be able to elaborate on the pros and cons of the libraries, frameworks, and cloud-based tools available to expedite the development process.

How to Choose the Right Level for the Role

Cost is a major factor to consider when deciding on the level of expertise you’re going to pursue. If your project is short-term or of limited scope (for instance, an internal knowledge base for your team that leverages GPT), you may do just fine with a junior developer whose hourly rates are on the lower side, especially if there is a more senior team leader who can guide them through questions of design, performance, and optimization. Without proper guidance, you might discover that a junior developer’s work is less maintainable or scalable over time; this can become a budgetary concern if you find that the problem requires your development team to do serious refactoring in the future.

For many businesses, the mid-level “Goldilocks” candidate may be the perfect match: just the right balance of competency and cost. Such a candidate should have the drive and initiative to grow their skills as they grow your product. If you can afford to invest in their career for the long term, you may see them grow into a senior-level developer who can go the extra mile and lead more junior colleagues through the codebase and help them make productive contributions. Or—budget permitting—you may simply choose to build a foundation for your project with a senior developer who’s able to hit the ground running on day one.

Another major consideration is the scale of the project at hand. Are you solving one particular problem for one particular moment, or are you building what you hope will be a long-standing platform? How will this project contribute to your overall business goals? Perhaps you’re looking to convert a spreadsheet full of product data into a series of blog articles—this is a task that’s fairly straightforward to accomplish with GPT once it’s set up properly. If this is a one-time migration, then the project timeline should be clear enough for a junior developer to manage it on their own. On the other hand, if you’re building a platform that will perform this task as part of a SaaS product, your search should start with mid-level candidates (if not higher). Less experienced candidates may not have the depth of experience necessary to be able to anticipate (or account for) problems in the code that might only arise at scale.

Common Use Cases for OpenAI Developers

Custom search – One of the most common OpenAI API implementations is to provide an interface so users can interact with the API in the context of an organization’s products or services. For example, you might use GPT to create a resource hub for your organization, so users can prompt it like a search engine and get results that are trained exclusively on your product’s documentation.

Customer feedback analysis – GPT is also great for problems of classification or sentiment analysis. For example, you could train it on customer feedback and let it surface especially positive or negative comments by deciphering emotional content from the language.

Translation – Another useful application of GPT is in translation, which refers to transforming natural language into formats that computers can work with more easily. An example of this would be a script that pulls text from Google Docs and converts it into JSON objects that can be parsed by a web server and stored in a database. These kinds of use cases are sometimes overlooked in favor of more exciting customer-facing products, but they can potentially have a big impact on your business operations by automating away some of the more tedious aspects of data manipulation.

Creative AI applications – DALL-E, OpenAI’s model that generates images from text descriptions, offers some unique opportunities for creativity, both on the developer and the user sides of an app. Take, for example, an online store that has hundreds or thousands of products but a poor catalog of product photography. An OpenAI developer could take a spreadsheet seeded with product descriptions and the best available product photos and deliver a set of consistently formatted images to serve as realistic placeholders. As consumers we tend to only notice AI-generated content when it’s poorly done; it’s a testament to the power of the technology that many AI images go undetected by casual viewers.

Content creation – Writers will tell you that AI is no substitute for human creators, but developers can leverage GPT to build content generation tools for blogs and social media to make writers’ lives easier. A good example of this would be a WordPress plugin that provides the author with an interface to generate summaries of their posts in a variety of formats suitable for sharing across multiple social networks. This can be quite tedious work for a publishing team, and GPT can handle the hardest parts.

What are the most important OpenAI Developer interview questions?

When conducting interviews, look for experience with APIs and full-stack web development broadly, as well as hands-on experience with OpenAI or alternative models (where applicable).

Aside from technical skills, it’s important to assess a candidate’s soft skills. Use behavioral interviews to gain insight into the candidate’s collaboration skills—effective communication is often equally as important as a diversity of tech expertise and is a must as project complexity grows.

Can you tell me about a project you’ve worked on that involved solving a business problem with AI (whether from OpenAI or otherwise)?

If a candidate has prior experience working with AI, invite them to give you an overview of a project that stands out to them and their role within it; their answer will indicate their seniority and leadership capabilities. Less experienced developers may have only played a minor part in the integration (such as helping to assemble a user interface for an AI app), while a more senior developer may be able to speak to big-picture questions of architecture and infrastructure. The best candidates will place an emphasis not on the technical implementation itself but on how it made a positive impact on business outcomes like customer engagement.

Can you describe a non-AI API integration project you’ve worked on that’s comparable in scale or complexity to our project? What kinds of challenges did you face?

Regardless of what kind of data an API lets you interact with, the actual work of API integration largely remains the same. There are standards and best practices like REST to adhere to and basic security concerns to address such as ensuring that API credentials are never exposed in the browser or on version control platforms like GitHub. Then there’s the question of cost when a provider charges a fee based on the number of API calls (as with OpenAI), which means you have to be mindful about how your app makes use of API data to avoid excess charges. Some of these things are simple enough to manage for hobby projects but become increasingly complicated as a project scales up and needs to serve hundreds or thousands of users. This question should help you assess a candidate’s problem-solving strategies and determine how relevant their prior experience may be to the role at hand.

Which OpenAI models should we use for our application?

Ask this question to gauge the developer’s depth of knowledge about the trade-offs associated with OpenAI models and versions: The right developer will be familiar with the different models offered by OpenAI and the pros and cons of each. For example, GPT-4 Turbo is the most advanced language model at the time of this writing, but the token cost is several orders of magnitude more expensive than GPT-3.5 Turbo. Seasoned OpenAI developers might even be aware that they can still use GPT-2, which can be an exceptionally cost-efficient choice if speed is not a priority. The answer to the question of which one to use, then, comes down to power, price, and time. A strong candidate will know how to appropriately weigh the pros and cons, and will ideally be able to draw on their experiences observing real-world usage in production apps.

Can you explain why ChatGPT streams responses instead of displaying the answer all at once?

Streaming in this context refers to the way in which ChatGPT renders words on the screen one syllable at a time. This provides users with what feels like instant gratification: instead of an indefinite loading screen, they get a response that appears to be generated in real time. This is a practical measure on OpenAI’s part, as it delays the time between user prompts, cutting down on the overall load on the system. However, one of the biggest disadvantages of the streaming approach is that it’s more difficult to moderate content. Beyond being a potential safety issue for users, illegal and harmful content goes against OpenAI’s approved usage guidelines and can lead to suspension or even termination of a project. A more junior developer might not know the answer to this question. That’s not a deal-breaker; you can learn a lot about their thought process if you invite them to speculate about why it might work this way. But a candidate who’s experienced with OpenAI models should be able to speak at length on this topic.

Why do companies hire OpenAI Developers?

Interest in AI has grown exponentially in recent years, and this growth shows no signs of slowing anytime soon. The rate of change and improvements to the technology mean that new capabilities for business innovations with AI are emerging faster than most organizations can keep up. The value of implementing AI solutions today will likely only compound over time as the tools at our disposal become more sophisticated.

Software solutions that harness OpenAI’s cutting-edge technology have the potential to revolutionize the way we engage with information online. Customers are beginning to expect ChatGPT-style interfaces in many of the products and services they rely on every day. By embedding OpenAI into your development services, your organization can accelerate digital transformation and maximize opportunities across industries like e-commerce, finance, and healthcare. The sooner you overcome the hurdles of finding the best OpenAI developer for your organization, the sooner you’ll be able to tap into the potential of the tech and give your users more natural and intuitive ways to solve their problems.

The technical content presented in this article was reviewed by Matei Copot.