The adoption of AI is causing a huge shift in the job market. And no, we are not here to make doomsday-like predictions; we’d rather shed some light on the opportunities that come with a staggering growth of the market value – did you know it is expected to reach $826 billion in 2030?
Companies of all sizes have been keeping their finger on the market pulse and are already investing in AI solutions, with enterprises looking to hire entire in-house teams to handle AI matters.
AI is not restricted to the work with LLMs and software development. The influx of job openings is triggered by AI applications spanning across industries – finance, healthcare, manufacturing, and retail – all of which require logical thinkers who are also subject matter experts.
However, as you’re browsing the LinkedIn Jobs section, the sheer variety of positions and titles gets the newcomers a bit confused. It’s quite easy to find yourself confused by the differences between AI developers, AI architects, ML engineers, data scientists, data analysts… some of these sure sound similar, don’t they?
Still, each requires a specific skill set and specialization to fit the role, and these are also some of the most attractive and lucrative careers out there.
Let us tell you a bit more about what’s out there.
The AI journey is challenging, but it can also be rewarding. Most generally speaking, people who pursue a career in AI and data science are innovative thinkers and creative problem-solvers, with enviable technical skills and ethical sensitivity.
The left side of the brain will have to be 100% involved, as most companies look for candidates with a bachelor’s degree in computer science, engineering, or a related field. This foundational education involves a ton of practice with mathematical and statistical skills, which are essential for problem-solving and are integral to any AI role. More specifically, AI jobs require familiarity with quantitative analysis to draw meaningful conclusions from the big data.
Problem-solving also requires knowledge of calculus, algebra, and probability, which are the backbone of many AI algorithms and techniques.
Then, to select and optimize these algorithms for specific problems, candidates need to be proficient in advanced ML techniques and deep learning. For more senior roles, one might be expected to demonstrate knowledge of neural networks, necessary for developing AI systems capable of complex tasks.
An AI enthusiast should also be familiar with the fundamental AI and ML techniques:
Furthermore, familiarity with Natural Language Processing (NLP) allows for the processing and analysis of text data using methods like machine translation, sentiment analysis, and language generation. Finally, knowledge of computer vision techniques is essential for tasks involving image and video analysis, such as facial recognition, object detection, and image classification.
Regardless of the position, every AI professional is expected to possess (at least the basic) knowledge of C++, Java, and most importantly – Python. Specialized roles require the use of specialized libraries, and Python comes with a large number of libraries, which makes it a critical component for developing AI applications.
We also recommend adding R programming language proficiency to the list of skills, as you’ll need it to master different data cleaning and manipulation methods.
Data management software like SQL (Structured Language Query) is required since it facilitates dependable management of databases and easy data access.
Finally, because the scalability of an AI product is of the highest priority, you need to demonstrate knowledge of cloud computing and cloud-based services, like AWS, Azure AI, and Google Cloud AI.
Like with any other profession, potential employers will want to see how well you apply the skills that cannot be so easily trained; the skills that you have intrinsically; skills like curiosity, communication, and business acumen.
These, in some form or another, will be put to the test daily, regardless of your role. Though they work from the shadows, soft skills are critical for your professional success and, consequently, the quality of your performance.
There’s a high degree of compartmentalization in the AI market, which has created specializations in the required roles. Positions are varied and split based on the various methods to handle data – for instance, data analyst, data scientist, and data engineer – all of which involve using AI as a tool and are largely focused on handling data to derive valuable insights.
Roles like AI developers, researchers, and architects are responsible for creating AI algorithms, researching capabilities of AI technologies, and detecting how they can be used on a grander scale.
But one of the most sought-after roles is that of a machine learning engineer, who shares the responsibilities of data scientists and software engineers but is focused on the use of ML algorithms.
Don’t worry – we know this is not nearly enough information to get you started, so let’s look at the most common AI job roles and typical expectations listed with each.
AI developers are responsible for designing, developing, and deploying AI-based systems. They are also expected to know how to build the accompanying infrastructure of AI models for transforming and ingesting data and creating data lakes.
Their tasks revolve around the use of AI and programming to provide solutions to specific challenges, for which they need logical skills, mathematics, and statistics. AI developers are also required to exhibit expertise in programming languages, specifically Python and R, as they are the core of the majority of AI systems. They are also expected to be familiar with fundamental ML concepts and have experience in text mining, data pre-processing, and data cleaning.
Artificial intelligence is a rapidly evolving and competitive field, and the most successful AI developers stay updated with the latest advancements in the field and come up with new ways to improve existing infrastructure.
AI architects are highly skilled developers, typically with an average of five years of experience in related roles. They play a crucial part in building and managing AI infrastructure, ensuring it can scale and adapt to the company's and market's evolving needs.
AI architects oversee the entire data-to-insight-to-action process, which requires a comprehensive understanding of the company's operations. They must excel in data visualization and problem-solving and have deep knowledge of machine learning concepts such as predictive modeling, unsupervised learning, heuristic optimization, graph theory, and classification problems.
Additionally, AI architects handle secondary objectives, ensuring that AI solutions are both effective and secure, reliable, and easy to use. These aspects are crucial for maintaining user trust and achieving seamless integration within the company's existing systems.
AI product managers are responsible for overseeing the strategy and development of an AI product, from the earliest stages through the launch (and beyond). They need a deep understanding of AI and ML lifecycles and also demonstrate the leadership skills necessary to lead cross-functional teams.
AI product managers work at the intersection of business and technology to ensure the product meets customers’ needs and fulfills business goals. One thing to keep their mind on at all times are ethical and regulatory challenges associated with AI products.
The road to becoming an AI research scientist is not an easy one. It requires raw talent and significant time investment – actually, it is the only role on this list where candidates are expected to obtain a PhD in computer sciences or a related field and publish multiple papers in respected journals throughout their professional career.
AI research scientists push the boundaries by conducting studies and experiments to improve existing AI models and develop new algorithms to solve complex problems. For this, they need to be skilled in practical data analysis and basic programming.
AI research scientists never work alone and are typically employed in leadership roles where they are expected to drive the development of novel ML techniques to meet unique client and business needs. As team leaders, they need to know how to communicate effectively, collaborate across disciplines and departments, manage time and project deliverables, as well as inspire and mentor their junior colleagues.
Machine learning engineers are considered a more generalized role and can use different AI concepts in conjunction with each other. It is often referred to as a somewhat mid-level position to which it is possible to transition from a similar one.
Because the problems that the organization is looking to solve typically require the knowledge of multiple specializations, ML engineers also have to be subject-matter experts in the sub-field and experts in devising AI solutions.
Like in most other AI roles, ML engineers have to know how to work with data management software (most commonly Hadoop or Spark) and need to be proficient in R, Python, and C++, particularly libraries like PyTorch and TensorFlow. But note that the knowledge of specific libraries and sub-fields required is dependent on the job specifics and the solution you will be employed to build.
Candidates looking to advance should also consider mastering NLP, computer vision, visualization, and deep learning.
Data science is the career of the future; the one every millennial parent hopes their child will pursue. A data scientist has a high earning potential but is required to spend a lot of time building a solid foundation in terms of education and work experience.
To be a successful data scientist, one has to be formally trained in mathematics, computer sciences, economics, or any other field with a strong computational focus.
The role is designed to help companies that are wrestling with great volumes and varieties of data that contain information vital to their business operations. For this reason, a data scientist needs to know how to work with data management software and conduct large dataset analysis in the cloud (like Hive, Spark, Hadoop, or Pig).
Data scientists apply cutting-edge AI technologies to solve real-world problems. They bring structure to large, formless, unstructured quantities of data, making its analysis possible. Day-to-day tasks include clearing and organizing big data, and then relying on statistics to identify meaningful patterns.
How? By employing ML, descriptive statistics, deep learning, and analysis to derive insights from data and answer businesses' needs. Data scientists also need to be familiar with different types of data wrangling and visualization software, like MatLab, and Tableau, as well as programming languages like Python and Java.
Above all else, the best data scientists need to be ‘curious’ and continue to dig beneath the surface of the problem to come up with a hypothesis to test.
As the name suggests, data analysts are tasked with analyzing data; determining its larger impact on the company, discovering ways to supercharge business operations, and providing all this information to key stakeholders.
This is why data analysts work hand in hand with data scientists. While data scientists are more focused on creating models, machine learning, and predictive analytics, analysts take over when it’s time to derive insights from data and present it in an easy-to-understand way. In other words, their job is to help translate any findings into non-technical jargon. This can include anything from presenting insights into the market to smaller-scale findings relevant to a specific product improvement.
They don’t have to be such well-versed programmers, but they need to demonstrate strong data visualization and reporting skills. Most importantly, data analysts have to be strong communicators, as they will be presenting insights and findings to the stakeholders, pointing out the implications of data for products, and informing decisions and processes. Their success is measured by the ability to convey the information accurately and facilitate more efficient decision-making.
We are also seeing an increased need for prompt engineers because, despite generative AI’s attempts to mimic human responses, it still requires precise instructions to generate high-quality and relevant results.
This is where prompt engineers enter the stage and direct GenAI to produce desired outputs by selecting the best formats, words, phrases, and symbols. By systematically designing and refining prompts, engineers provide GenAI with a larger context and detailed instructions so that it can achieve more meaningful and usable outputs.
In other words, they make AI apps more efficient.
Sure, LLMs are capable of performing a variety of tasks, like answering questions, completing sentences, summarizing documents, or translating texts. But no matter how large, all these language models can do is predict the best output based on past training.
And if you have ever played around with ChatGPT, you know that not all inputs produce satisfactory results.
The benefits of prompt engineering are two-fold:
Prompt engineers ensure the AI understands the intent better with minimum input, thus reducing the number of errors and providing users with accurate and relevant responses.
It is most commonly used in applications that require AI to:
Inviggo is always on the lookout for talented and ambitious minds to join our team and help us build advanced AI products. We value diverse experiences and perspectives and are eager to grow our team with new ideas and innovative approaches. We believe in continuous learning and growth, and we're excited to work with individuals who share our passion for pushing the boundaries of AI technology.
Featured Photo by Lukas
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