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Do you need to hire AI developers and are wondering how to go about it?
Many, many organizations are already racing to take advantage of artificial intelligence. Studies indicate that AI could contribute a staggering $15.7 trillion to the global economy by 2030. It is a booming industry that is already touching everyone.
AI technologies have already made their mark in diverse sectors like manufacturing, banking, finance, agriculture, healthcare, e-commerce, etc. You’ve all heard of ChatGPT, so you will know at least one case study.
Companies use various AI technologies to help transform in-house functions like marketing, customer service, cybersecurity, data analysis, etc. However, they often lack the AI skill sets or don’t have a large enough in-house team to execute complex, strategic AI projects. These organizations need talented AI developers to deliver their projects.
Good AI developers can effectively develop AI solutions that are both powerful and accurate. However, hiring AI engineers without either the skills, experience, or motivation required can derail a project. This makes your hiring decisions extremely important.
You might think it’s enough to hire freelancers. However, this might end up exposing your AI engineering project to high risks. Instead, you should strongly consider outsourcing top-notch AI engineers from trustworthy software development companies like DevTeam.Space to ensure the best chance of success. More on this later.
Before you make any hiring decisions, you need to understand your project’s skill requirements. Get in touch if you need help with this.
To help you, we have put together a list of the general AI developer skills that you will need:
Primary skills required by an AI engineer
An ideal AI engineer needs expertise in the following broad primary skill areas:
A. Programming languages for AI development
We recommend that you use Python as the programming language of choice. For many developers, Python is the preferred tech stack for AI and machine learning development.
We recommend it because Python is an open-source programming language that offers excellent libraries for AI development. Developers should have skills in well-known libraries like TensorFlow, Scikit-learn, NumPy, SciPy, Theano, Pandas, Keras, PyTorch, Matplotlib, etc.
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Do you plan to use AI for a data science project? Data scientists widely prefer Python for these popular libraries.
Java, C++, JavaScript, Julia, and LISP are other programming languages that are used for AI development. Developers should also know how to use suitable IDEs (integrated development environments).
B. Databases and big data
AI developers should know important SQL and NoSQL database management systems. MySQL and PostgreSQL are two prominent open-source SQL databases. MongoDB and Apache Cassandra are well-known NoSQL databases. We recommend that you hire AI and machine learning engineers that know Hadoop, an important big data framework.
C. In-depth AI technology skills
Developing AI solutions often involve various AI capabilities, e.g.:
- Machine learning (ML);
- Natural language processing (NLP);
- Computer vision;
- Speech-to-text and text-to-speech.
You need developers with experience in any AI capability that is relevant to your project.
Machine learning is an important one among cutting-edge technologies in the AI space. You will most certainly need AI developers with ML skills. A competent machine learning engineer should know the following:
- Important machine learning algorithms;
- How to create machine learning models;
- Deep learning, neural networks, recurrent neural networks, etc.;
- How to deploy ML and AI models.
Note: If you plan to execute a data science project, then you need AI developers with relevant experience. You might need experienced data scientists. Since data scientists use data visualization tools like Power BI extensively, you should specify such skills in your job ad. AI developers working on such projects need data engineering and predictive analytics skills too.
Other software development skills needed by expert AI engineers
Expert AI engineers need the following additional skills:
A. Information and application security skills required by AI developers
AI/ML/NLP/computer vision engineers need plenty of data for building and testing AI/ML models. Furthermore, they can deploy AI models only after training them with massive data sets. Naturally, the presence of large data sets creates questions about data security. You must build AI systems that securely process data and only allow access to authorized users.
AI developers need practical knowledge of information security, e.g.:
- Implementing data encryption;
- Incorporating modern tools like MFA (multi-factor authentication);
- Fixing bugs and application security vulnerabilities that can be exploited by hackers;
- Using firewalls, antivirus, etc.
- Fixing security and compliance testing issues;
- Modern tools like multi-factor authentication, digital signatures, etc.;
- Using security tools provided by cloud computing platforms.
B. Industry domain knowledge required by artificial intelligence engineers
You might be planning an AI project to transform certain business processes within your company. Furthermore, you might need to incorporate highly customized business requirements. Talented AI developers with in-depth knowledge of your industry can make a big difference in such an example. Specify your industry domain knowledge as a requirement in your job posting.
Note: DevTeam.Space developers assigned to your project will come with an extensive background in developing applications within your specific industry.
C. Code review skills needed for delivering an AI project
Projects using artificial intelligence, machine learning, natural language processing, or other AI capabilities involve large-scale coding. You need a robust code review process for quality management. Look for developers who know the code review process back to front. Look for programmers with knowledge of code review strategies.
D. Software engineering skills required by AI specialists
Talented AI developers need a thorough understanding of software engineering. They should understand coding best practices. Developers need knowledge of the Agile methodology. AI engineers must have a good knowledge of testing, defect prevention, etc.
E. Software architecture skills needed for executing AI projects
AI developers need to understand software architecture. Artificial intelligence projects are complex. Sound knowledge of software architecture helps developers to navigate the complexities.
F. Competencies that the best artificial intelligence engineers need
An AI developer needs the following soft skills to succeed on complex projects:
- Teamwork;
- An ability to understand the standpoint of the end-user;
- Commitment;
- Passion for excellence;
- Problem-solving skills;
- Communication skills.
How to find experienced AI engineers?
Having understood the skill requirements, initiate the hiring process. Take the following steps:
1. Choose the right platform for hiring top artificial intelligence engineers
Companies develop AI solutions to meet a wide range of specific business objectives. For them, a lot is at stake. Businesses need high-quality people to ensure the success of their projects. Average or below-par developers can derail your project. This underlines the importance of making the right hiring decisions.
You might think that hiring freelancers is a good idea to save money. Freelance platforms do allow you to negotiate a low hourly rate. We emphatically don’t recommend this for AI development projects.
You will likely find it hard to manage freelancers. They work only part-time on your project, therefore, you might not get enough focus from them. Freelance platforms also don’t offer any project management support and you will need to find replacement developers if your freelancers leave your project mid-way.
To ensure you get the best value for your money, you should consider hiring full-time AI engineers from a trustworthy software development company like DevTeam.Space.
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Our developers have relevant expertise in your industry and work full-time for our platform. They are highly experienced and motivated. We train them on our AI-powered Agile process which ensures they are able to efficiently onboard and offboard while also maintaining our high standards of excellence at all times.
DevTeam.Space provides project management support. Our consistent focus on quality mitigates any risks. As part of out contract, we only release a developer’s payment once you are happy with their work. Read our reviews on Clutch to find out what our clients are saying about us.
2. Interview candidates
After choosing a hiring platform, you posted your job ad. Now you need to interview your candidates. Use our set of interview questions if you need help:
Ask questions that help you evaluate the hands-on skills of your candidate AI engineers. Assess how they solved problems on their past projects. Describe your project and ask candidates how they would approach it.
Top Tip: You need to really test your candidates during the skills interview. We recommend that you get someone who is a high level Flutter developer to assist you. We like to ask follow up questions to our candidate’s answers to see how much they really know. Get a skilled Flutter developer to help you with the interviews.
3. Onboard AI engineers
Onboard the AI developers that you hired. You need to take the following steps:
- Share project documents and explain your project.
- Grant access to developers.
- Introduce the new team members to your existing team.
- Describe the project schedule to developers.
- Explain the work approval process.
- Set up a communication process with the new developers and establish accountability.
Useful tips to hire AI engineers
Keep the following aspects in mind when hiring artificial intelligence developers:
A. Hire a capable team leader if you hire remote AI engineers
A huge number of organizations have built fantastic products by engaging remote AI developers. You can do so too. However, you need a competent team leader, ideally with experience in managing remote teams.
Note: For companies hiring developers from DevTeam.Space, we provide a dedicated account manager who is tasked with managing all the developers that you hire. If you hire a team from us then you won’t need to hire a PM to manage the team unless you want to do so.
B. Check whether developers have experience in cleaning data for training and testing AI models
High-quality data is important in AI/ML projects. You’d be surprised the number of businesses that use low-quality, unprocessed data. You need AI developers that know how to prepare data for testing and training.
AI engineers need to understand data structures well, furthermore, they must also know how to analyze unstructured data. They should also understand how to cleanse data. Check whether candidates have this experience as it is vital.
C. Avoid hiring developers from multiple hiring platforms
Large and complex AI projects often require several developers. If you need to hire multiple developers, then you might think of hiring from different platforms. If you do this then you may well spend considerable time trying to integrate their remote work.
Instead of having to deal with a nightmare like this, we strongly recommend that you hire a complete AI development team from a reputable company like DevTeam.Space instead. Such a team is cohesive and easier to manage, and since ours all work for us full time and come with a manager, hiring from us will save you a great deal of headaches.
Sample interview questions to use when you hire AI developers
Ask questions that help you evaluate the hands-on experience of AI engineers, e.g.:
A. Describe your experience of developing virtual assistants.
Answer:
This is an open-ended question. Developers should talk about what they found in the requirements, e.g., specifics of user behavior. They should describe the various challenges they faced. These challenges could include technical and other issues. Candidates should explain how they analyzed the root causes of the issues. They should explain how they solved the challenges.
B. Describe the information security challenges you faced in an AI project.
Answer:
In response to this open-ended question, expect developers to describe the kind of sensitive data used in the project. They should then describe the data security threats they encountered. Developers should talk about the information security solution they designed and implemented.
C. Talk about your experience of implementing a machine learning algorithm.
Answer:
When replying to this open-ended question, developers should describe a summary of the project requirements. They should then explain how they chose an ML algorithm to meet these requirements. Developers need to describe the data cleansing work that they did. They might have used a library of a popular programming language like Python, so they should describe how.
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D. Can you explain the concept of transfer learning in the context of training AI models, and how does it benefit the development process?
Answer:
Transfer learning is a technique in machine learning where a pre-trained model, usually trained on a large dataset, is fine-tuned for a specific task using a smaller dataset. Instead of training a model from scratch, transfer learning leverages the knowledge learned by the pre-trained model, which often includes features and patterns that are generally useful across tasks.
This approach speeds up training and requires less data for the new task. The benefits of transfer learning include faster convergence, better generalization, and the ability to handle new tasks with limited data. It’s crucial for AI engineers to choose the right pre-trained model architecture and adapt it effectively to the target task.
E. Explain the concept of attention mechanisms in deep learning models. How have they revolutionized the field of natural language processing (NLP)?
Answer:
Attention mechanisms are a mechanism in deep learning models that allow the model to focus on different parts of input data with varying levels of importance. In NLP, attention mechanisms have played a pivotal role in improving the quality of language models, especially in tasks like machine translation and text generation.
The mechanism enables the model to pay selective attention to specific words or phrases during different steps of processing, allowing it to capture long-range dependencies and improve the contextual understanding of the input text.
Transformer-based models, like the BERT and GPT architectures, have gained popularity due to their effective utilization of attention mechanisms, leading to significant advancements in various NLP tasks.
F: Describe the trade-offs between using CPUs and GPUs for training AI models. When would you choose one over the other, and how can you optimize the utilization of each?
Answer:
CPUs and GPUs have distinct strengths and weaknesses when it comes to training AI models. CPUs are better suited for tasks that require sequential processing and high single-threaded performance. They are essential for tasks involving data preprocessing, model orchestration, and scenarios with limited parallelism.
On the other hand, GPUs excel at parallel processing, making them ideal for deep learning training tasks due to their ability to process multiple computations simultaneously. GPU clusters are commonly used for training large-scale models.
To optimize utilization, developers can employ data parallelism techniques that distribute batches of data across multiple GPUs for training. Additionally, tools like CUDA and cuDNN help optimize GPU performance by harnessing their parallel processing capabilities effectively. The choice between CPUs and GPUs depends on the nature of the task, the size of the dataset, and the available hardware resources.
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Developing an AI-driven solution can involve lots of hard work that requires expert knowledge of AI tools and implementation practices.
Planning and executing AI projects aligned with your organizational AI strategy can be highly complex exercises. Many factors influence the outcome of such projects, and a great software development team is one of them. We can’t overstate the importance of competent developers. You need to hire the best artificial intelligence engineers.
DevTeam.Space is a community of experienced field expert designers, engineers, and top AI professionals. Our dedicated developers are fully vetted and work for us full-time. We train them to use our unique AI-powered Agile process, which not only streamlines the development process but makes onboarding them and offboarding them much easier.
We provide experienced and motivated AI developers with expertise in your industry segment.
Wondering how we can help you? Fill out our DevTeam.Space product specification form and one of our experienced account managers will get in touch to answer any questions that you might have.
