Register for our upcoming webinar:  

Why Tech Screening Fails to Uncover True Talent

Thursday | May 30, 2024 1pm ET

Register Now

How To Hire an AI Developer

AI developers build intelligent systems that can process vast amounts of data, learn from patterns, and make informed decisions. They design, build, and implement AI algorithms and models that can automate tasks, predict trends, and optimize complex processes. They can create cutting-edge applications such as chatbots for instant customer support, recommendation systems for personalized content, and even autonomous vehicles.

We are all familiar with how AI has enabled personalized content recommendations. Streaming platforms like Netflix and Spotify use AI algorithms to analyze your preferences and suggest movies, shows, or music that you're likely to enjoy. In healthcare, AI has proven to be a game-changer. Imagine a doctor being able to accurately diagnose medical images like X-rays and MRIs with the help of AI algorithms. This has become a reality, as AI-powered systems can analyze these images to spot diseases like cancer at an early stage, improving the chances of successful treatment.

AI Developers: Why are they needed?

AI developers are highly sought after by a variety of industries and companies. Tech giants like Google, Microsoft, Amazon, Apple, and Facebook are leading the charge in AI research and development. Financial institutions such as JPMorgan Chase, Goldman Sachs, and Morgan Stanley are integrating AI into trading algorithms and risk assessment. Healthcare and pharmaceutical companies like Pfizer, Johnson & Johnson, and Roche are utilizing AI for drug discovery and personalized medicine.

The automotive sector, represented by companies like Tesla, General Motors, and Toyota, is investing in AI for self-driving cars. Amazon, Alibaba, and Walmart are using AI to enhance customer experience and recommendations. Entertainment and gaming, aerospace and defense, consulting and technology services firms, all highlight the global demand for AI developers.

Skills required

  • Python, R, C++
  • Mathematics and Statistics
  • Machine Learning
  • Natural Language Processing
  • Data Handling
  • Cloud Computing

Job titles

  • Machine Learning Engineer
  • Data Scientist
  • AI Research Scientist
  • Computer Vision Engineer
  • Deep Learning Engineer
  • AI Solutions Architect

AI Developer Interview Assessment

The most common assessment framework for AI Developers on HackerEarth includes

A combination of technical evaluations, coding exercises, and behavioral assessments. Here are some common components of the assessment framework:

Code completion tasks

Code completion tasks are invaluable for assessing the technical skills of AI developers. To hire exceptional developers, you can delve into candidate's expertise in machine learning algorithms, deep learning frameworks, and data processing tools. You can ask candidates to complete tasks related to data preprocessing, model training, and optimization. It can help you hire AI engineers with an understanding of AI techniques and ability to implement complex solutions.

Multiple-choice questions (MCQs)

Multiple-choice questions (MCQs) are a valuable tool for assessing the knowledge and problem-solving abilities of AI developers. They allow you to efficiently evaluate a wide range of technical skills and domain expertise. Thus help you find AI developer whose skillset aligns with your job requirement. You can cover topics like machine learning algorithms, data preprocessing, model evaluation, and AI frameworks. To hire the right developer you should tailor these questions according to your organization's tech stack.

Project-specific questions

If the job role requires certain skill sets, then it should also be included. These questions can range from algorithms and techniques to hands-on coding exercises depending on the project requirement.

Job-specific questions

AI developers can be asked different kinds of questions based on the job role. For a Machine Learning Engineer, questions would center around ML algorithms, model evaluation, and feature engineering. Deep Learning Engineer interviews will delve into neural networks, CNNs, RNNs, and libraries like TensorFlow. For an NLP Specialist questions might range from tokenization, word embeddings, and libraries like spaCy. Whereas AI Research Scientists could be asked to tackle advanced algorithms and theoretical foundations.

Our bias-free, objective platform has facilitated 1,00,000+ assessments

Steps to Create an AI Developer Assessment on HackerEarth

  1. Log in to Hackerearth assessments
  2. Add questions from our library and create a test
  3. Send invites to candidates
  4. Evaluate our AI-assisted report and shortlist candidates

Assess AI developers for free

Click here now

Sample Questions to Ask an AI Developer

  • Write a program to load a CSV file named into a pandas DataFrame. (Data Preprocessing)
  • Implement a basic neural network architecture using TensorFlow/Keras with an input layer, a hidden layer with 64 neurons, and an output layer. (Neural Network Creation)
  • Write code to perform feature selection using the chi-squared test from Scikit-learn. (Feature Selection)
  • Write code to perform sentiment analysis on a given text using the TextBlob library. (Sentiment Analysis)

  • Which type of learning involves providing the model with labeled training data?
    1. Supervised Learning
    2. Unsupervised Learning
    3. Reinforcement Learning
    4. Semi-Supervised Learning

  • What is the primary function of the activation function in a neural network?
    1. To normalize input data
    2. To adjust learning rate
    3. To introduce non-linearity
    4. To compute loss function

  • What is the process of converting words into their base or root form called?
    1. Lemmatization
    2. Tokenization
    3. Stemming
    4. Normalization

  • Could you describe the steps you would take to preprocess a dataset before training a machine learning model?
  • How do you choose an appropriate evaluation metric for a classification problem? What metrics would you use for an imbalanced dataset?
  • What is the purpose of cross-validation, and why is it important when training machine learning models?
  • Can you differentiate between bias and variance in the context of model performance? How do they impact the model?

  • Can you explain the process of tokenization using NLTK? Why is tokenization important in NLP?
  • How does NLTK handle stemming and lemmatization? What's the difference between the two?
  • What are stop words, and how can NLTK be used to remove them from a text document?
  • Could you demonstrate how NLTK is used for part-of-speech tagging? Why is part-of-speech tagging valuable in NLP?
  • NLTK provides sentiment analysis capabilities. How can you perform sentiment analysis on a text using NLTK?

These sample questions provided here are for interviewing early-experience talents. For more customized assessments for candidates with higher experience, you can contact our team at or request a demo here.

Guidelines for Writing Job Description for AI Developers

Job title

In the AI developer job description, begin with a clear and concise job title that reflects the role, such as "AI Developer", "Machine Learning Engineer", "Deep Learning Engineer", and "AI Research Scientist". Candidates often use specific keywords when searching for job opportunities, and a well-defined job title will match those keywords more effectively. It can help you hire right individuals and thus help you hire AI engineers whose skillset matches with the job requirement.

Job summary

In the AI developer job description you should also provide a brief overview of the position, clarifying the essential responsibilities and objectives. Describe the role's primary focus and how it contributes to the company's goals. The job summary is a chance to introduce your organization, its culture, and its mission. This can be important in order to hire AI engineers who are not only looking for the right job but also the right company culture and values.


Outline the specific tasks and responsibilities that the AI developer will be expected to handle. This may include:

  • Develop and implement machine learning algorithms for various applications, such as natural language processing and image recognition.
  • Train, fine-tune, and evaluate machine learning and deep learning models using libraries like TensorFlow and PyTorch.
  • Engineer and extract relevant features from data, optimizing models for accuracy and efficiency.
  • Write efficient and maintainable code in languages like Python, utilizing frameworks such as Keras.
  • Stay updated with the latest AI advancements and emerging technologies to drive innovation.

This is the most important part of AI developer job description where you mention the tasks a developer might have to handle.

Skills and qualifications

List the essential AI developer skills and qualifications that candidates must possess. This can change depending on the job role:

  • Proficiency in TensorFlow, PyTorch, Scikit-learn, strong Python coding skills, Data preprocessing, feature engineering, model evaluation metrics, and hyperparameter tuning (Machine Learning Engineer)
  • Computer vision techniques, algorithms, image and video data preprocessing, Convolutional Neural Networks (CNNs), object detection, image segmentation, and image-based model interpretation (Computer Vision Engineer)
  • NLP concepts, libraries (NLTK, spaCy), Recurrent Neural Networks (RNNs), Transformers, Language embeddings, transfer learning, text data preprocessing, vectorization (NLP Engineer)

The skills might vary depending on the type of role you are hiring for. Therefore, it's important to list out the skillset that your project requires in the job description.

Preferred skills

Mention any additional AI developer skills or qualifications that would be beneficial but not mandatory for the role. For example, while it's not mandatory for all AI developer roles, experience with NLP can be a valuable asset. This allows you to find AI developer who may bring additional expertise to your AI team. Moreover, in the AI developer job description, you can also ask for experience with specific tools, libraries, or frameworks.

Education and experience

Specify the educational background and professional experience required for the position. This could range from a bachelor's degree in computer science or a related field to several years of relevant experience. Clearly defined requirements can deter unqualified candidates from applying, saving time and resources during the candidate selection process. It can filter out candidates beforehand and help you find devs with right AI developer skills and experience.

Company culture and EVPs

Briefly highlight your company's culture, values, and any unique perks or benefits offered to employees that can help attract candidates who align with your company's ethos.

Application instructions

Provide instructions on how candidates can apply for the position. Include where to send their resume, portfolio, or other required documents. Also, specify the deadline for applications, if applicable.

Equal Opportunity Statement

Include a statement affirming that your company is an equal opportunity employer committed to diversity and inclusion. It can enhance your company's reputation and attract candidates who align with your values.

You can always make your job descriptions more gender-neutral by following simple steps like using gender-neutral pronouns like ‘they/them’. It is also a good practice to map your developer persons to your job profiles before you post an open role.

Download our guide for writing better developer personas

How to Conduct AI Developer Job Interview

Skill-first hiring requires that the developer interview process be designed to understand the candidate’s skill and experience in the given domain. You can consider the following guidelines when conducting a face-to-face interview with an AI developer:

Conduct a real-time technical assessment

Apart from the take-home assessment, including a real-time skill evaluation during the interview process can give you valuable insights into how well a candidate communicates and thinks through problems. Additionally, the live interview lets us explore how candidates make decisions, adapt to different situations, and work well with others. These aspects are important for their success in the team and the role they're being considered for.

HackerEarth's interview platform, FaceCode, offers valuable tools to enhance your assessment process. It features a built-in question library that allows you to gauge applicants' familiarity with Machine Learning concepts. The platform includes an IDE environment, enabling you to evaluate their coding skills for clarity, efficiency, and maintainability. Additionally, you can leverage pair programming to observe candidates' problem-solving methods, logical thinking, and attention to detail.

Understand the candidate’s project experience and review the portfolio

During the AI developer interview assessment, ask them about their past or current projects. Find out about their work experience and how they contributed to those projects, focusing on the skills needed for the job. Have them explain a project they did related to AI and ML, describing what they did and their responsibilities. You can also look at their portfolio or code samples to see how they write code, document their work, and how good their projects are.

Understand if they are a good culture and team fit

While technical skills are essential for a developer, the significance of cultural fit should not be overlooked. During the AI developer hiring assessment, it's important to keep in mind taht the ideal candidate must possess not only accurate coding abilities but also effective communication and collaboration skills to thrive in agile and collaborative environments. Evaluating a candidate's capability to work both independently and collaboratively in a team is crucial. This assessment provides valuable insights into the candidate's AI developer skills, problem-solving skills, communication, and adaptability.

Additionally, this evaluation offers engineering managers a chance to exhibit their team culture and values, enabling them to find a candidate who aligns well with the team's ethos.

Start free trial

30,000+ recruiters and hiring Managers across 1000+ businesses grow their tech teams with HackerEarth



As an AI recruiter, seek candidates holding at least a bachelor's degree in Computer Science, AI, Data Science, or related fields. But also don’t prioritize a degree over exceptional problem-solving skills. You should prioritize strong programming skills, notably in Python, and a robust comprehension of intricate machine-learning concepts and algorithms. Familiarity with AI frameworks such as TensorFlow and PyTorch should be a plus.

You should look for candidates who can effectively develop and fine-tune complex models, demonstrate advanced problem-solving acumen, and exhibit a drive for continuous self-improvement. A compelling AI developer will also showcase sophisticated skills in neural networks, deep learning architectures, natural language processing, and reinforcement learning, along with experience in cloud services and familiarity with big data tools like Hadoop or Spark.

It's not necessary for candidates to possess experience in all the necessary frameworks; being well-versed in at least one major framework like TensorFlow, PyTorch, or Scikit-learn is generally sufficient for an AI developer role. Demonstrating expertise in a core framework showcases a solid foundation in AI development. However, candidates with proficiency in multiple frameworks may offer added flexibility and adaptability.

Different AI roles often correspond to specific tools and frameworks. For instance, a Machine Learning Engineer might benefit from proficiency in TensorFlow, PyTorch, and Scikit-learn, while a Computer Vision Engineer could excel with knowledge of OpenCV and CNNs. For an NLP Engineer, familiarity with NLTK, spaCy, RNNs, and Transformers could be essential. A Data Scientist's toolkit might include pandas, Matplotlib, and predictive modeling techniques. The choice of framework depends on the role, but competence in core frameworks remains a valuable asset.

A Machine Learning Engineer has a unique set of skills tailored for crafting and operationalizing machine learning models. They're adept at using programming languages like Python or R to write code that instructs computers on how to learn from data. Think of them as data whisperers who transform raw information into insightful predictions.

These engineers also possess a knack for mathematics, as they use mathematical concepts like algebra and statistics to instruct models. For example, when predicting house prices, they'll use historical data to teach the model to anticipate costs based on various features. Additionally, Machine Learning Engineers are skilled in deploying these models to the real world. Take the example of your smartphone's camera recognizing objects in your surroundings.

Evaluating a candidate's real-world project-handling skills involves assessing their ability to apply theoretical knowledge in practical scenarios. This can be done by reviewing their project portfolio and discussing specific projects during interviews. For instance, if hiring for a Machine Learning Engineer role, candidates could showcase projects involving data analysis, model creation, and result interpretation. During discussions, inquire about their decision-making process, challenges faced, and how they collaborated with others. Requesting code walkthroughs and asking about the impact of their projects on solving real problems can provide insights into their hands-on capabilities. By exploring their project experiences, you can gauge their adaptability, problem-solving, collaboration, and innovation skills.

The significance of AI and ML varies across job roles. For Data Scientists and Machine Learning Engineers, these technologies are central to their tasks, involving data analysis and model development. Software Engineers benefit from AI and ML knowledge to create intelligent applications, while Business Analysts leverage them to extract insights from data. Product Managers incorporate AI and ML for product innovation, while roles like Computer Vision Engineers and NLP Engineers heavily rely on these technologies for specialized tasks. Data Engineers use AI and ML to optimize data pipelines, and AI ethicists address ethical concerns. Healthcare specialists utilize AI and ML for diagnostics and personalized treatment. The depth of expertise varies, aligning with each role's specific goals and requirements.