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Artificial intelligence systems are making a big difference already, and experts believe that AI will be even more important in the future. Statista estimates that AI will create $4.38 trillion business value in 2025.
A vast branch of computer science, AI includes many capabilities. Some prominent capabilities are machine learning (ML), natural language processing (NLP), speech recognition, image recognition, image processing, etc. Many industries like manufacturing, healthcare, banking, financial services, cybersecurity, etc. have prominent AI uses cases.
Organizations of all kinds are creating new opportunities and improving business processes using AI. They are taking up strategic software development projects to do that.
Many organizations have created a comprehensive AI strategy. Their strategies include data science, analytics, big data, and business process management in addition to AI. Many of them increasingly plan to utilize advanced AI capabilities like deep learning, neural networks, etc.
Naturally, AI engineers have a high demand. AI engineering jobs are plentiful, and AI engineer salary figures are high! Consequently, hiring AI engineers can be hard after posting AI engineering jobs on all hiring channels.
Thankfully, you have a few options at hand to hire competent AI engineers. You need to choose the right hiring platform.
The best option for startups or enterprises that are undertaking AI projects is to hire developers from a software development company such as DevTeam.Space.
Before we delve deeper into the pros and cons of each type of platform, let’s take a closer look at the technical skills that you will need from your AI engineer.
Artificial engineering programming skills
An AI engineer might need several of these primary technical skills:
- Knowledge of any one of the programming languages like Python, Java, and Julia;
- Familiarity with important AI/ML libraries offered by the corresponding programming language, e.g., Scikit-learn in Python;
- In-depth knowledge of one or more of the AI capabilities like computer vision, image recognition, speech recognition, machine learning, natural language processing, etc.;
- Sound understanding of machine learning algorithms like K-means clustering algorithm, support vector ML algorithm, artificial neural networks, linear algebra, linear regression ML algorithm, random forest ML algorithm, etc.;
- Good understanding of types of ML algorithms, e.g., supervised learning, unsupervised learning, etc.;
- Knowledge of processing unstructured data;
- Understanding of data cleansing and preparing data pipelines;
- Familiarity with AI models;
- Understanding of machine learning models like hidden Markov models;
- Knowledge of how to deploy AI models;
- Familiarity with AI infrastructure and development platforms like Google AI Platform, Microsoft Azure Machine Learning Platform, etc.
Note: These are all must-have skills for AI engineers. Senior-level AI engineers need more advanced skills.
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Advanced skills required in senior-level AI engineering jobs
Senior-level AI engineers need the following skills:
- Knowledge of big data technologies like Hadoop, Apache Spark, etc.;
- Solid understanding of statistical analysis;
- Firm understanding of programming algorithms offered by the relevant languages;
- Good understanding of data engineering;
- Knowledge of deep learning algorithms;
- Understanding of important models like Gaussian Mixture models used by data science teams;
- Good knowledge of artificial neural networks like recurrent neural networks;
- Deep understanding of machine learning techniques like linear discriminant analysis;
- Knowledge of developing a machine learning algorithm;
- A thorough idea of business process management;
- Knowledge of how data scientists work with different AI models and ML algorithms.
Senior AI and machine learning developers need to gain a thorough understanding of how data engineers work. They might need to learn programming languages like R that are used by data scientists.
Other software development skills required by AI engineering professionals
AI and machine learning engineers need the following additional general skills:
- In-depth understanding of software engineering;
- Thorough knowledge of software development methodologies like agile, waterfall, etc.;
- A practical understanding of machines learn;
- Sound knowledge of systems engineering;
- Solid understanding of application security and tools like encryption;
- Familiarity with API development;
- Knowledge of human-centered design;
- Hands-on knowledge of code review;
- Understanding of software quality management and defect prevention;
- Familiarity with important software architecture patterns;
- Knowledge of how to meet non-functional requirements like performance, scalability, maintainability, etc.;
- Considerable track record of developing high-visibility and complex business applications successfully.
Note: Large-scale and complex AI projects routinely utilize cloud computing platforms to meet their computing power requirements. Therefore, you need AI engineers with a good understanding of cloud computing.
Competencies required by artificial intelligence and machine learning engineers
AI/ML engineers should have the following competencies or soft skills:
- Passion for excellence;
- Commitment to the project objectives;
- Customer focus;
- Communication skills;
- Problem-solving abilities;
- Business acumen;
- The ability to see the big picture;
- Teamwork;
- Collaboration skills.
How to find the best artificial intelligence developer?
You now know about the skills that AI/ML engineers need. Initiate the hiring process, which involves the following steps:
1. Choose the right platform to hire artificial intelligence specialists
AI is one of the highly evolving technologies in the tech industry. Experts continue to “push the envelope”, and they try to impart the capabilities of the human brain to AI systems. Many of the innovations in AI involve highly specialized technologies. This makes AI projects complex.
You need the right people in an AI/ML development team. The choice of hiring platform becomes crucial for this.
You might think of hiring developers from freelance platforms. Freelance platforms enable you to negotiate and get a low hourly rate. However, we don’t recommend this approach for complex projects.
Freelancers work part-time on your project. You might not get enough bandwidth from them. They continue their job search for higher pay after they start your project, and freelancers often leave a project mid-way.
Freelance platforms don’t provide any management support. You need to find replacement developers in the case of turnovers.
We recommend you hire dedicated AI engineers from trustworthy hybrid software development companies like DevTeam.Space. Our screening process ensures that you get high-quality developers.
Our AI engineers typically work on several complex projects, and they gain valuable experience. We routinely encourage our developers to upskill. Therefore, you get motivated software engineers.
We provide management support. At DevTeam.Space, we implement physical and technical safeguards to protect your data.
2. Interview the candidates
You now need to interview the candidates. The interview might cover multiple technologies like Python, SQL, Hadoop, etc. We at DevTeam.Space provides comprehensive interview questions and answers, e.g.:
Use our interview questions and answers.
Ask questions that help you to assess the relevant skills. Focus on hands-on knowledge instead of theoretical knowledge. Check how the AI engineers collaborated with various stakeholders.
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Find out how the candidates solved AI development project problems. Explain your project requirements and ask how the candidates will approach them.
3. Onboard AI engineers effectively
After interviewing the candidates, you have selected the right AI engineers. You now need to onboard them effectively so that they can be productive.
Explain the project requirements to the new AI developers. Provide the relevant project documents, furthermore, answer questions from the developers.
Provide access to the project’s technical environment. The new AI engineers should get access to code repositories and other relevant tools.
Introduce the new programmers to your existing team. Explain the various roles and responsibilities. You also need to explain the responsibilities of the AI engineers to your in-house team.
Describe the project plan and milestones. Take care of all NDA (non-disclosure agreement) requirements.
Set up a communication process with the new engineers. Explain the project milestone approval processes, furthermore, establish accountability.
Tips for interviewing artificial intelligence engineers
You can hire expert AI engineers with the help of the following interviewing tips:
A. Decide early about the AI capabilities you will utilize
AI is a vast field, and you might use more than one AI capability in your project. E.g., you might need to use machine learning with natural language processing. Choose the AI capabilities early.
You will find it very hard to hire AI engineers with considerable experience in all of the important AI capabilities. Deciding early about the required capabilities helps you to interview candidates effectively.
B. Check whether the candidates understand how to design and build secure AI systems
Problems like data poisoning can considerably reduce the effectiveness of an ML system. Wrong data can make the ML models grossly inaccurate. AI systems can make wrong decisions based on such wrong ML models. Cyber-attackers look for many other ways to attack AI/ML systems.
You need AI engineers with experience in designing and building secure AI/ML systems. Proactively look for it during the interview.
C. Evaluate the knowledge of data preparation
AI systems can mimic human behavior or make other important decisions based on “training”. The volume and quality of past data considerably influence the quality of this training.
You might not get data in the most suitable format though. AI engineers often need to prepare data for training and testing AI systems. Data preparation is a complex process, and it requires extensive knowledge. Evaluate this skill when you interview the candidates.
Samples of questions when interviewing artificial intelligence engineering professionals
Ask questions that help you to evaluate the hands-on knowledge when you hire AI engineers. The following are a few examples:
A. Describe your experience of implementing machine learning models
In response to this open-ended question, expect the candidates to describe how they used ML development libraries offered by programming languages. AI developers should describe the data preparation steps they undertook. They should also talk about testing.
B. Explain how you used big data tools in AI projects
This is an open-ended question. Candidates should describe how they used Hadoop, Apache Spark, HDFS, etc. They should explain the challenges they faced and how they resolved them.
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C. Describe your experience of using cloud platforms for AI development
Candidates should describe the kind of cloud platforms they used for AI development. You should expect them to describe the architecture of the AI systems they developed. They should describe how they utilized DevOps tools provided by the cloud platform.
D. How do you collaborate with a data science team as an artificial intelligence engineer to ensure seamless integration and effective communication in AI projects?
As an AI engineer, I regularly engage with data science teams to understand their requirements, exchange insights, and validate machine learning models for proper integration into AI applications.
E. Can you explain your experience in developing AI models for self-driving cars or autonomous vehicles?
During my previous role, I was part of a team that developed advanced machine learning algorithms to enable self-driving cars, contributing to the safe navigation and decision-making capabilities of these vehicles.
F. How do you utilize complex networks and perform statistical analysis to optimize AI algorithms for specific applications?
I leverage complex network analysis to identify patterns and dependencies within data, and then employ statistical techniques to optimize machine learning algorithms, resulting in improved accuracy and efficiency.
G. Describe a recent project where you implemented AI applications through embedded code in hardware devices or systems.
In a recent project, I embedded AI algorithms into edge devices, enabling real-time decision-making capabilities and reducing reliance on cloud-based processing, thereby improving response times and data privacy.
H. How do you ensure the successful implementation of AI applications to derive valuable business insights and support critical decision-making processes?
As an AI engineer, I collaborate closely with stakeholders to understand business objectives and requirements, aligning AI solutions accordingly to deliver actionable insights for informed business decisions.
I. What is the average annual salary range you expect for an artificial intelligence engineer with your level of expertise?
AI engineer salaries vary quite considerably, especially due to geography. Based on my qualifications and experience, I believe the average annual salary for an AI engineer with my expertise would fall within the range of [provide a specific range based on market research and location].
J. How do you pull data from various sources and design application program interfaces (APIs) to facilitate data exchange between different components of an artificial intelligence AI system?
I use an application program interface API to establish communication between disparate systems, enabling seamless data transfer and integration, and ensuring that the AI models receive the required data inputs for optimal performance. The need to build AI models that are sound and to implement models that are fully able to cope with the certain tasks that they are needed for is paramount. To this end, I test machine learning models extensively before considering implementing AI applications.
Remember that the suggested answers are general and may vary based on the candidate’s specific experiences and background. Conducting
Submit a Project With Zero Risk
If you are looking for expert AI engineers to help you build and implement your project, DevTeam.Space is a great place to start. We have a large community of field expert AI developers with all the right skills and experience. Let our AI engineers develop your application with your existing team.
Product managers who are not technically inclined or are not familiar with software programming can enjoy the advantage of our dedicated and complimentary account managers who are assigned to help you to manage the developers ns your overall project progress.
You are more than welcome to contact us by filling out a DevTeam.Space product specification form to ask about our expert AI engineers. After filling out the form, one of our dedicated account managers will get back to you to answer any questions you might have.
