AI Jobs Bulgaria
Over the next 10 years, the Bulgarian state will invest Bgn 170 million in the Institute for Computer Science, Artificial Intelligence and Technology – INSAIT – which was established in partnership between Sofia University and two of the leading technological universities in the world – the Swiss ETH Zurich and EPFL. This is an excellent opportunity for technology development and providing AI jobs in Bulgaria.
Some of world’s leading technology companies such as Google, DeepMind and Amazon Web will invest over BGN 12 million over the next 3 years. In addition, the institute has attracted funding of BGN 14 million from local Bulgarian IT technology entrepreneurs and investors.
This is great news for Bulgaria and will sure to increase the need for AI talent in the country. This article looks at the AI job types that will be created and the skills required.
Increasing popularity of AI
AI develops intelligent software and systems based on how human minds think, learn, decide, and solve a problem. It enables machines to perform human-like functions by learning through experience.
AI is a broad term, encompassing general artificial intelligence, machine learning, expert systems, data mining, and more. In today’s world, AI capabilities are in high demand across industries—gaming, robotics, face recognition software, weaponry, speech recognition, vision recognition, expert systems, and search engines.
Top Artificial Intelligence Skills
There are a varied number of skills that you need to master, all of which require training. As there is a wide variety of career options available in AI, knowing which skills are the most sought after can help you land on the job you want. The most common factor in all these fields is that they all require a great deal of experience in math and science-related topics.
Here are the top artificial intelligence skills that you need to have:
- Programming languages
- Linear algebra and statistics
- Signal processing techniques
- Neural network architectures
Important Programming languages for AI
- Python is widely used in AI and machine learning due to its simplicity, code reliability, and faster execution.
- Java is also used extensively in AI for implementing mappers and reducers, intelligence programming, genetic programming, search algorithms, neural networks, ML solutions, and more.
- R: You need R for statistical computation, numerical analysis, machine learning, neural networks, and more. R allows you to collect and organize data sets, apply ML and statistical functions, and use matrix transformations and linear algebra for data processing.
- C++ is used in AI to enable procedural programming and manipulating hardware resources.
Apart from programming languages, you must also understand various frameworks and libraries. It will help you write quality code at a faster speed. Some of the highly useful in AI are TensorFlow, SciPy, NumPy, Scikit-learn, Apache Spark, PyTorch, and more.
Linear algebra and statistics
- Linear and abstract algebra form the basis of many parts of AI like machine learning and computer vision.
- Statistics involves data collection, interpretation, and analysis and coincides with data science.
- Probability: You must possess sound knowledge of probability and probability distribution to have a good career in AI.
- Graph: Knowing how to look at graphs and understand what it conveys is needed in AI.
Signal Processing Techniques
Signal processing may include time-frequency analysis, convolution, Fourier Analysis, and other AI concepts. The applications of signal processing are feature extraction like speech recognition and image understanding, audio signal processing as in music or speech representation.
Neural Network Architecture
Neural networks mimic human brain-behaviour to solve complex problems. They are used for adaptive control, predictive modelling, regression analysis, pattern recognition, data processing, and trainable applications. Examples of use are speech recognition, vehicle control, game playing like chess or racing, sequence and pattern recognition, medical diagnosis, data mining, financial applications, email spam filtering, and more.
Machine Learning Engineer
Machine learning engineers must possess strong software skills, be able to apply predictive models, and utilize natural language processing while working with massive data sets. Also, machine learning engineers are expected to know software development methodology, agile practices, and the complete range of modern software development tools
Preferred Qualifications: modern programming languages like Python, Java, and Scala.
Robotic Scientist
Robots can automate jobs, but they require programmers working behind the scenes to ensure they function well. Robotic science is used for multiple functions from space exploration, healthcare, security, too many other scientific fields.
Preferred Qualifications: Advanced mathematics, physical sciences, life sciences, computer science, computer-aided design and drafting (CADD), physics, fluid dynamics and materials science, and related AI certification.
Data Scientist
Data scientists collect, analyse, and interpret large amounts of data by using machine learning and predictive analytics to gain insights beyond statistical analysis.
Preferred Qualifications: In-depth knowledge of SAS and R, Python coding, Hadoop platform, experience working on cloud tools like Amazon’s S3, and the ability to understand unstructured data.
Research Scientist
A research scientist is an expert in multiple artificial intelligence disciplines, including machine learning, computational statistics, and applied mathematics including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation.
Preferred Qualifications: Parallel computing, artificial intelligence, machine learning, knowledge of algorithms, and distributed computing, and benchmarking.
Business Intelligence Developer
The primary job is to analyse complex data and look for current business and market trends, thereby increasing the profitability and efficiency of the organization.
Preferred Qualifications: Data warehouse design, data mining, knowledge of BI technologies, SQL queries, SQL Server Reporting Services (SSRS), and SQL Server Integration Services (SSIS) and popular data science certifications.
