By 2030, it is anticipated that Artificial Intelligence (AI) will generate approximately USD 13 trillion in economic value worldwide. Many companies in various industries and sectors have recognized the potential value of AI and are already on their way to becoming AI-centric. Whether it’s tech firms developing advanced AI technologies such as self-driving cars and intelligent speakers or traditional businesses utilising AI for mundane tasks like fraud prevention or automating customer service, there is no denying the significant commercial impact that AI technology can offer.
Therefore, once you decide to incorporate AI technology into your business, you must find people who can develop it. In this article, we will provide you with all the information on why creating a remote team of artificial intelligence engineers is a great option make and do it by giving you a ready-to-use guide.
AI Developer Skills Requirements You Should Look for
When creating a team, it is strongly advised to consider remote work. Whether it is because of its cost benefits or the fact it is a new preference for employees and employers, it is a new standard in the IT business and it is the best solution for many companies. See a better explanation in our AI development cost comparison.
Therefore, to build your AI team, you need to start somewhere. The first thing you need to know is the different AI specializacions on the market, and let’s examine them:
Knowledge of Artificial Intelligence and Machine Learning:
Artificial Intelligence is a complex and constantly evolving field, with various types and subsets of AI. An AI developer should possess a solid theoretical understanding of the field and their specialization in particular. Some examples of AI and ML are:
- Deep Learning:
To model and solve complex problems, ML Engineers often employ Deep Learning or artificial neural networks. These networks are inspired by the structure and function of the human brain and are more complex, layered, and abstract than older machine learning models. Deep Learning techniques include supervised, unsupervised, and reinforced learning to train deep neural networks on large amounts of data, allowing the networks to learn and improve independently. Real-world examples of Deep Learning include Tesla’s auto-pilot system, recommendation systems by Google, Spotify, and Amazon, and more recently, DallE and ChatGPT.
- Computer Vision:
Computer Vision is a field that enables computers to interpret and understand visual data, such as images and videos. By leveraging large amounts of data, Computer Vision helps computers recognise faces, comprehend ideas, track motion, and make sense of visual information.
- Natural Language Processing (NLP) and Natural Language Understanding (NLU):
NLP combines linguistics and coding to enable computers to understand, interpret, and generate human language. Examples of NLP include AI translation, email categorisation, text-to-speech technologies, chatbots, and text sentiment analysis. NLP refers to the broader field and refers to the pre-processing of text to convert unstructured data into structured data. NLU focuses on interpreting context, deriving intent, and meaning from words.
If you’ve ever asked Siri to convert 10,000 won to dollars, used autocorrect to change the spelling of “orfhid” to “orchid,” or typed “time Paris” into Google to find out “What time is it in Paris right now?”, you’ve used NLP.
While an AI developer’s job includes traditional coding and data engineering, it’s crucial to prioritise finding a strong engineer with coding skills in one or more of the following programming languages:
- Python is a widely-used, high-level programming language that is well-suited for ML development due to its simplicity and consistency. It has many libraries and frameworks for ML, such as TensorFlow, PyTorch, and sci-kit-learn, making it easy to implement ML models.
- Julia is a high-level programming language similar to Python, gaining popularity in AI/ML.
- C/C++ are also a popular choice for AI developers. They offer low-level control and are often used for computationally intensive tasks or for building systems where performance is critical.
- Java/Scala is often used in enterprise applications, large-scale software development, and distributed systems. Java and Scala were associated with the Big Data Era and have less relevance to many AI applications today.
- R is a programming language and environment for statistical computing and visualization. It is widely used for data analysis and has several libraries and frameworks for ML, such as caret, mlr, and randomForest.
Proficiency in AI tools and technologies
It’s important to note that AI technologies and tools are constantly evolving, so this list may need to be more comprehensive and up-to-date.
However, here are some popular tools, libraries, frameworks, and concepts that can be relevant when hiring AI engineers:
- Deep Learning Frameworks/Libraries: TensorFlow (including Keras), PyTorch, Jax, Caffe, MxNet, CNTK
- Traditional ML Libraries: Scikit-learn, XGBoost/lightgbm, Vowpal-wabbit, etc.
- NLP: Natural Language Toolkit (NLTK), spaCy, Gensim, CoreNLP, OpenNLP, Flair, UIMA, Huggingface, BERT, Transformers, Attentions, etc.
- Computer Vision: OpenCV, sci-kit-image, SimpleCV, YOLO, Vision API, etc.
- Algorithms: Supervised, unsupervised, semi-supervised, self-supervised, and reinforcement learning. Active learning, transductive learning, transfer learning, adaptive learning, multi-task & multi-modal learning, federated learning & meta-learning, etc.
- ML model families: Neural Networks (ANN), Graphs, Trees, Generalized Linear Models (GLM), Kernel Machines
- Big Data Technologies: Apache Spark, Storm, Hive, Kafka, Flink, Hadoop, Arrow, Iceberg
- ML Infrastructure/Platform: Data Orchestration: MLFlow, Kubeflow, Airflow, Prefect, etc. Embedded/Mobile ML: Core ML, AI accelerators, FPGA/GPU/TPU/APU, 8bit, etc.
- AI governance principles: Transparency, Explainability, Visibility, Fairness, Robustness, Safety, Integrity, etc.
It’s essential to consider your company’s specific needs when looking for an AI engineer with expertise in particular technologies.
What does an AI development team look like?
To help you navigate through the haze and get the first look at creating an AI team on your own, we can present you with the essential pieces of information. Here is the overview of the components that a basic artificial intelligence team should consist of :
- Data Team: This team is responsible for consuming, handling, and evaluating data. The Data Team is typically made up of Data Scientists, Data Engineers, and Machine Learning Engineers.
- Algorithm/Research Team: Deep Learning and ML Engineers make up this team, which is responsible for driving the creation of your AI foundation. As businesses expand, they may add a Research Team that explores new ways of solving business problems by developing AI. This team consists of ML Researchers, Computer-Vision Researchers, and Natural Language Processing Researchers.
- Infrastructure/Platform Team: These engineers are responsible for creating, managing, and expanding the infrastructure supporting software products. Backend Software Engineers, Infrastructure Engineers, Platform Engineers, MLOps DevOps Engineers, Site Reliability Engineers, Security Engineers, and System Engineers are all part of the Infrastructure Team.
- Application Team: This team creates the app or product that interacts with customers. It comprises Frontend and Fullstack Engineers, UI/UX Designers, and Product Managers.
Best Practices for Hiring Remote AI Engineers
There is no universal approach to scaling a remote team, as the strategy varies depending on the organization’s unique needs. Hiring remote AI engineers requires careful planning and execution to ensure that the organization can find and attract top talent while also ensuring the candidates are a good fit for the team and the company culture. Some of the best practices for hiring a remote AI team are:
- Create a clear job description and requirements: Clearly define the specific skills, experience, and qualifications needed for the position. This way, you can avoid unsuited candidates and save some time and money.
- Utilize multiple sourcing channels to find top talent: Use various sourcing channels such as job boards, social media, and professional networks to attract the best candidates. This way, you can have a more varied talent pool and better chances to find the right fit.
- Streamline the application and interview process: Simplify the hiring process to ensure timely and transparent, which could turn off potential candidates. It can also benefit your company as the process will be more precise and less time-consuming.
- Use technical assessments and coding challenges for AI engineers: Test candidates’ technical skills by having them complete reviews and coding challenges. This lets you see their skills in action and evaluate them more accurately.
- Assess both technical and soft skills: Evaluate candidates not just based on their technical skills but also on their communication, problem-solving, and teamwork abilities. Those factors are also essential and can be crucial characteristics of a good employee.
Where can you hire an AI engineer?
AI and ML engineers have become essential as businesses strive to stay competitive in a data-driven future. Major tech companies are dedicating entire departments to AI research or acquiring AI startups to integrate into their operations. To implement AI into your organisation, hiring the best AI and ML engineers available is crucial. Though finding qualified candidates may be difficult, there are numerous resources available to streamline the hiring process.
You can have freelancers & in-house developers and find them:
- Turing: is a platform that uses its AI-backed Intelligent Talent Cloud to help companies quickly source, vet, match, and manage the best remote software developers across the globe. It’s used by top companies like Pepsi, Dell, and Coinbase and offers pre-vetted, highly-qualified talent with over 100 skills. The process only takes 3-5 days, making it an excellent option for companies looking to construct an engineering team quickly.
- Toptal: is a platform that connects companies with the top 3% of talent globally. Its extensive screening process for AI and machine learning engineers takes 0-3 weeks, and you can hire talent based on your specific needs. Toptal has served over 6,000 customers, including big names like HP Enterprise, JPMorgan Chase, and Airbnb. You can hire teams on an hourly, part-time, or full-time contract basis, and the platform offers a discovery process to help ensure that you source the best talent for your needs.
- Manatal: simplifies the hiring process for AI talent through its AI Recruitment Software. It automates redundant tasks and suggests the best candidates for a job. The platform is designed for HR teams, recruitment agencies, and headhunters and offers features like sharing job openings on multiple channels, managing sponsored job advertising campaigns and enriching candidate profiles with data from LinkedIn and other social media platforms.
You can outsource:
Then, what if this task is too much for you and you have no time to look for AI engineers on your own? The best solution for you is to hire an outsourcing team and let them do the work for you! This way, you can save time and money and be sure that the team will be full of skilled and experienced developers.
The best places to look for outsourcing companies are:
Communication and Collaboration Best Practices for Remote AI Teams
Once you hire the right people for your team, the real work starts. Setting up the correct workflow and atmosphere in your group is extremely important and will affect the success of your project. Effective communication and collaboration are crucial for the success of remote AI teams. Here are some best practices for facilitating these critical components:
Using communication tools effectively:
Remote teams need to use the appropriate communication tools, such as video conferencing, messaging apps, and project management software, to ensure that team members can communicate easily and quickly. By selecting the right tools, teams can communicate effectively and foster collaboration regardless of geographic location.
Setting clear expectations and goals:
Remote AI teams should establish clear expectations and goals to avoid confusion and promote accountability. This includes outlining project timelines, individual responsibilities, and expected outcomes. With clear guidelines and plans, team members can work towards the same objectives and stay on track throughout the project.
Fostering a culture of trust and accountability:
Trust and accountability are crucial for remote AI teams to work effectively. Leaders should establish a culture of trust by communicating openly and transparently while promoting individual accountability and ownership of tasks. When everyone is on the same page, it’s easier to build a strong sense of trust and responsibility, even in a remote setting.
Encouraging collaboration and knowledge sharing:
Collaboration and knowledge sharing are essential for remote AI teams to thrive. Leaders can encourage this by providing opportunities for team members to work together, share ideas, and learn from one another. This creates a sense of belonging and shared purpose, allowing for stronger team connections and better outcomes.
Conducting regular team meetings and check-ins:
Regular team meetings and check-ins can keep remote AI teams connected and ensure that everyone is on the same page. Leaders should schedule regular appointments and check-ins to provide updates, discuss progress, and identify any challenges that need to be addressed. This helps keep everyone aligned and accountable, making it easier to address issues as they arise.
Best Practices for Managing Remote AI Teams
Setting up the main rules for teamwork is essential as a first step. Don’t stop at that point. You must keep your finger on the pulse to have an excellent technology development team. Managing your developers can be challenging, but here are the best practices that will help you:
Setting up effective workflows and processes:
Remote AI teams need well-defined workflows and processes to ensure they are aligned and working effectively. Leaders should establish standard communication, collaboration, and project management procedures to help streamline the team’s operations.
Utilizing project management and agile methodologies:
Project management and agile methods can help remote AI teams manage projects more efficiently. By using these methodologies, leaders can ensure that everyone is working towards the same goals and has clear expectations regarding project timelines, milestones, and deliverables.
Performance management and feedback strategies:
Performance management and feedback strategies are crucial for remote AI teams to stay on track and continuously improve. Leaders should provide regular feedback and offer resources for growth and development to help team members improve their skills and contribute to their success.
Time management and scheduling for remote teams:
Remote AI teams may have different work schedules or time zones, making time management challenging. Leaders should establish clear guidelines for scheduling and managing time and ensure that team members have access to the resources they need to work effectively.
Providing resources and support for remote workers:
Remote AI team members may require additional resources and support to work effectively. Leaders should ensure that team members have access to the tools and resources they need, including communication tools, software, and other necessary equipment. Additionally, leaders should be available to provide support and guidance when required.
Tools and Technologies for Remote AI Teams
As every team who works remotely, AI developers are no different, just with a little plus. Besides standards of online office, such as communication with coworkers, they require access to specific tools and technologies to ensure they can work efficiently and effectively. Since the programs they use may not be common knowledge, we will help you get a grasp of the essentials:
Cloud Computing Platforms for AI Development
Cloud computing platforms provide remote AI teams with the necessary computing resources to develop and train models. Teams can access these platforms from anywhere, making them an essential tool for remote work. Some of the best examples are:
Collaboration and Communication Tools
Collaboration and communication tools are critical for remote teams, including remote AI teams. These tools allow team members to communicate in real-time, share files, and work together on projects despite being in different locations. The most common are:
Virtual Meeting Platforms
Virtual meeting platforms enable remote AI teams to hold meetings, collaborate, and discuss project progress in real-time. These platforms are essential for remote teams working across different time zones. Some of the best ones are:
Data Analysis and Machine Learning Tools
Data analysis and machine learning tools are essential for remote AI teams to analyze and process data, build and train models, and make predictions or recommendations. These tools help remote teams to work together on complex AI projects effectively. Here are some of the best examples :
By utilizing these tools and technologies, remote AI teams can work efficiently and effectively and achieve their goals, regardless of physical location.
Once you decide to incorporate artificial intelligence technologies into your project, you must find a team of people to develop it. Since remote work is a standard nowadays, especially in IT, consider this employment model. No worries, as it is a great solution and will solve old problems and bring many new opportunities.
To hire a team of skilled AI developers, you should plan your actions to avoid conflicts. Starting from the recruitment process, be sure to take time and put in the effort to avoid wasting time. Also, do not hesitate to utilise new technologies as well as strategies. The same goes for the communication rules you would like to imply to your new group of engineers. Finally, research to find the perfect tools for your company to enhance your and your employees’ efficacy and comfort.
If all of this still sounds too overwhelming or you need more time, you can always seek outsourcing companies’ help. This solution is even better as it saves you time and money. It also benefits you as you give your project to the professionals and you can lay back easily yet achieve the desired quality! For the best results and the most excellent process possible, contact SolveQ. Our company can create a team of skilled and experienced developers without your effort.