Artificial intelligence is influencing more and more areas of our lives and the demand for professionals in this field continues to grow. Jobs in the AI field are not in short supply – there are plenty of them. From recommendation algorithms in online stores to autonomous cars to intelligent medical diagnostics, AI specialists have their hands full.
AI work – table of contents:
In both large corporations and small businesses, there is a growing demand for experts who design and implement advanced algorithms and data analytics to enable companies to operate more efficiently. But what does the job of an AI work specialist look like, and why is it worth investing in such talent?
AI specialist. Definition and responsibilities
An artificial intelligence specialist is a person who combines programming knowledge with data analysis skills, applying modern machine learning (ML) and deep learning (DL) technologies. Their responsibilities include creating algorithms to automate processes or analyze large data sets.
Although artificial intelligence is a technical domain, there is also no shortage of people with less exacting talents among AI professionals. In addition to engineers, some specialize in AI ethics and law, as well as developers who use AI tools to create marketing content or chatbots. AI jobs also include project management, and education and training activities that allow others to use AI tools more and more efficiently.
However, let’s focus on the professions that constitute the closest center of AI specialists.
An AI engineer is a person who designs, builds and tests systems based on artificial intelligence, such as chatbots, voice assistants or computer games.
It focuses on developing tools, systems and processes that enable AI to be applied to real-world problems. The average salary in the US is about $113,000 per year (According to Glassdoor, 2022).
Examples of AI engineer responsibilities include:
- creation and management of AI development and production infrastructure – for example, a data management system aimed at improving artificial intelligence algorithms used in speech recognition applications,
- conducting statistical analysis and interpreting the results to improve the organization’s decision-making processes – for example, identifying mobile app usage patterns to improve recommendation algorithms,
- automating AI infrastructures for the data science team – for example, creating scripts and tools that automate the process of deploying AI models, enabling faster innovation into production.
Source: DALL-E 3, prompt: Marta M. Kania
Machine learning engineer
What does AI work involve for machine learning (ML) engineers? MLs are involved in designing AI systems responsible for machine learning and maintaining and improving them. In other words, they create and optimize algorithms that learn from data and automatically improve their performance. Among their responsibilities are:
- Implementation of machine learning algorithms – for example, development and implementation of advanced machine learning algorithms for an e-commerce product recommendation system,
- Conducting experiments and tests with AI systems – for example, organizing A/B tests for various predictive models to assess which one best predicts customer behavior,
- Design and development of machine learning systems – for example, creating a novel machine learning system that automatically adjusts marketing strategies in real-time based on analysis of market data.
It is thanks to their work that we can enjoy, for example, increasingly well-functioning voice assistants such as Siri and Alexa. Their salaries average around $123,000 a year.
Data engineers construct the infrastructure necessary to collect and process huge sets of information and oversee its flow and analysis to extract valuable information and knowledge from it. With this area of AI work, online stores can optimize their inventory based on sales forecasts generated by data-driven marketing systems.
Data engineers, or data engineers, build systems that collect, manage and transform raw data into useful information for business analysts and other professionals involved in interpreting data for business purposes.
The average annual salary here is $104,000.
Robotics engineers are working to create and program robots that can perform various tasks in a physical environment.
Their AI work is used in many industries. One of the more famous examples is the robots used to assemble cars on the production lines of automotive giants like Tesla and General Motors. The efficiency of robotics engineers therefore translates into vehicle quality and safety for car drivers and passengers. Annual salaries are typically around $99,000.
Source: DALL-E 3, prompt: Marta M. Kania
Is it possible to be simultaneously a great programmer, an experienced statistician, and have a deep understanding of the industry in which the company operates? In addition, can this person working in AI demonstrate excellent communication skills, presenting his analysis and forecasts with attractive infographics and charts?
These are the demands many companies place on data scientists.
With data, a data expert can help financial companies uncover hidden patterns of credit fraud or invest capital where historical data shows the highest probability of return on investment. Such an expert has an average salary of $113,000 a year.
AI Ethics Specialist
An AI ethics specialist deals with issues of morality and regulation related to artificial intelligence. The main areas of interest for a person doing such work in AI are:
- Studying and evaluating the impact of artificial intelligence on people, society, the environment,
- Developing ethical principles and standards for the field,
- Creation of the company’s AI policies and regulations for the use of tools provided by the company to end users,
- Ensuring the legality of solutions developed by the organization.
The support of such a specialist can be invaluable when integrating new technologies, allowing organizations to bypass PR risks and often legal problems that could arise when AI-based solutions are improperly implemented. On average, such an expert earns about $100,000 a year.
A prompt engineer is a person who creates and customizes texts or questions that are used to communicate with artificial intelligence-based systems or to stimulate their creativity.
This relatively new position involves recent developments in generative AI, such as language models (e.g., GPT-4). The prompt engineer is in charge of “talking” to these models to generate desirable, meaningful and ethical responses.
How can AI work specialists contribute to the growth of your business?
Creating your own or implementing off-the-shelf solutions based on artificial intelligence can quickly turn your company into a very modern organization. Working in AI is a difficult field, so the salaries of artificial intelligence specialists are substantial.
However, you can, thanks to them:
- automate business, innovative and creative processes saving time and money, and increasing the efficiency of operations,
- collect, organize and analyze data to better understand their customers, as well as the details of their production or logistics processes,
- conclude the data, and thus make more accurate business decisions, saving money.
Here are some examples:
- Predicting demand and optimizing the supply chain – enables more efficient inventory management and reduces costs,
- Marketing and sales automation, such as ad targeting – increases campaign effectiveness and improves ROI,
- Analysis of customer needs and satisfaction – helps tailor offerings to market expectations,
- Fraud detection and risk analysis – protects against financial losses and fraud,
- Customer service automation (chatbots) – improves customer service at a lower cost,
- Personalization of content and recommendations – increases engagement and sales through personalized offers,
- Creating a unique library of prompts to quickly generate PR content for the organization – making external communications easier and faster.
It’s worth considering where your company could implement AI work to optimize its processes or services for customers.
Hiring or outsourcing – how to manage AI talent more effectively?
Cost-performance analysis shows that for many small companies, it may be more profitable to work with a freelancer or an outside company than to hire and create a full-time in-house IT department to support AI-based systems.
Collaboration with independent specialists seems particularly attractive at the initial stage of AI work. This is because they avoid large initial investments in technology and human resources. At the same time, they give access to high-level specialists and ready-made solutions that can easily scale as the company grows.
However, it is worth having a long-term strategy in mind. If a company expands the use of artificial intelligence in many areas of the business, at some point it may be more cost-effective to build an in-house team to have full control over key business processes.
AI work – summary
Artificial intelligence opens up promising new career opportunities for professionals whose skills combine advanced technical knowledge with an understanding of business and customer needs.
Demand for such talent will grow as AI applications become more widespread in various industries. The unique combination of engineering and business skills makes working in AI one of the most interesting in the field of new technologies.
If you are interested in working in AI, now is the perfect time to start learning and building your project portfolio.
AI in business:
- Threats and opportunities of AI in business (part 1)
- Threats and opportunities of AI in business (part 2)
- AI applications in business - overview
- AI-assisted text chatbots
- Business NLP today and tomorrow
- The role of AI in business decision-making
- Scheduling social media posts. How can AI help?
- Automated social media posts
- New services and products operating with AI
- What are the weaknesses of my business idea? A brainstorming session with ChatGPT
- Using ChatGPT in business
- Synthetic actors. Top 3 AI video generators
- 3 useful AI graphic design tools. Generative AI in business
- 3 awesome AI writers you must try out today
- Exploring the power of AI in music creation
- Navigating new business opportunities with ChatGPT-4
- AI tools for the manager
- 6 awesome ChatGTP plugins that will make your life easier
- 3 grafików AI. Generatywna sztuczna inteligencja dla biznesu
- What is the future of AI according to McKinsey Global Institute?
- Artificial intelligence in business - Introduction
- What is NLP, or natural language processing in business
- Automatic document processing
- Google Translate vs DeepL. 5 applications of machine translation for business
- The operation and business applications of voicebots
- Virtual assistant technology, or how to talk to AI?
- What is Business Intelligence?
- Will artificial intelligence replace business analysts?
- How can artificial intelligence help with BPM?
- AI and social media – what do they say about us?
- Artificial intelligence in content management
- Creative AI of today and tomorrow
- Multimodal AI and its applications in business
- New interactions. How is AI changing the way we operate devices?
- RPA and APIs in a digital company
- The future job market and upcoming professions
- AI in EdTech. 3 examples of companies that used the potential of artificial intelligence
- Artificial intelligence and the environment. 3 AI solutions to help you build a sustainable business
- AI content detectors. Are they worth it?
- ChatGPT vs Bard vs Bing. Which AI chatbot is leading the race?
- Is chatbot AI a competitor to Google search?
- Effective ChatGPT Prompts for HR and Recruitment
- Prompt engineering. What does a prompt engineer do?
- AI Mockup generator. Top 4 tools
- AI and what else? Top technology trends for business in 2024
- AI and business ethics. Why you should invest in ethical solutions
- Meta AI. What should you know about Facebook and Instagram's AI-supported features?
- AI regulation. What do you need to know as an entrepreneur?
- 5 new uses of AI in business
- AI products and projects - how are they different from others?
- AI-assisted process automation. Where to start?
- How do you match an AI solution to a business problem?
- AI as an expert on your team
- AI team vs. division of roles
- How to choose a career field in AI?
- Is it always worth it to add artificial intelligence to the product development process?
- AI in HR: How recruitment automation affects HR and team development
- 6 most interesting AI tools in 2023
- 6 biggest business mishaps caused by AI
- What is the company's AI maturity analysis?
- AI for B2B personalization
- ChatGPT use cases. 18 examples of how to improve your business with ChatGPT in 2024
- Microlearning. A quick way to get new skills
- The most interesting AI implementations in companies in 2024
- What do artificial intelligence specialists do?
- What challenges does the AI project bring?
- Top 8 AI tools for business in 2024
- AI in CRM. What does AI change in CRM tools?
- The UE AI Act. How does Europe regulate the use of artificial intelligence
- Sora. How will realistic videos from OpenAI change business?
- Top 7 AI website builders
- No-code tools and AI innovations
- How much does using AI increase the productivity of your team?
- How to use ChatGTP for market research?
- How to broaden the reach of your AI marketing campaign?
- „We are all developers”. How can citizen developers help your company?
- AI in transportation and logistics