In the year 2000, inspired by the way that technology was becoming a central part of our everyday life, we created a company based on a simple philosophy, focused on helping people succeed – the people who work and engage with us and the people who use the systems and applications we design, build, and operate.
For more than 20 years, we have been working with some of the world’s leading brands in the fields of finance, insurance, telecommunications, media, technology, and retail.
We have been present in Serbia since 2015, and our offices are in Belgrade, Novi Sad, Kragujevac and Čačak. No matter the location, our people work on projects for clients worldwide and are part of multi-disciplinary, cross-location teams.
1. Why develop a career as an AI scientist?
- Traditional programming can solve a large number of problems, which are relatively well known.
- Machine learning, as a new field, offers new possibilities and provides the creation of adaptive solutions guided by experience.
- The demand for AI experts is growing exponentially, the daily progress of the field expands the possibilities of solving problems that were not solvable until then.
2. What employment and advancement opportunities do you have for that career path?
- Endava, as a multinational company, provides a wide range of positions that deal with data processing through various domains and projects.
- One of them is the ML engineer who deals with creating data flow throughout the life cycle of the ML process.
- Through career development, Endava enables a narrower specialization of one ML engineer, such as ComputerVision, AutoML, MLOps, NLP, etc.
3. What technologies do your developers apply most to projects?
- Endava’s approach is to use the most relevant methods, algorithms and tools for specific challenges.
- As a rapidly expanding field, machine learning requires constant improvement of current technologies.
- By comparing with currently known solutions and previous experience, a decision is made on the use of appropriate technologies.
- Commonly used libraries: TensorFlow, PyTorch, Keras, OpenCV, Scikit Learn, AutoML, SageMaker, H2O, etc.
- Architectures: GAN, R-CNN, YOLO, Transformer, Logistic Regression, Random Forest, Ensemble, etc.