Trainee Image Recognition Specialist

ABOUT COMPANY

SoftServe Business Systems was founded in 2003. We aim high; our mission is to lead the digital revolution in the FMCG industry. It means delivering the best products & services possible that empower businesses to grow and improve efficiency.

We are a product company, and this affects our day-to-day activities. Our team is highly involved in the client’s needs, we value business expertise, and we take every step with extra carefulness. Among our clients are businesses like AB InBev, Unilever, JDE, PepsiCo, Henkel, and others.

SoftServe Business System’s ideal candidate can share the company’s values and become a reliable partner for the team.

About the project

One of the products we develop is an AI-powered retail solution designed to verify whether products are placed correctly on store shelves according to merchandising standards.

After arranging products on the shelves, a merchandiser uses our mobile application to take photos of the display. The application then automatically analyzes the images and determines whether the product placement complies with the required planogram and merchandising guidelines.

Image Recognition Specialist, your role will be to help improve the accuracy of our Computer Vision models. You will annotate products in images, identify and label objects, and assign the correct attributes and product names. Your work will directly contribute to training and improving our AI models.

REQUIREMENTS

• Proficiency in Microsoft Office (especially Excel)
• Strong teamwork and collaboration skills
• Analytical mindset
• Attention to detail and a high level of accuracy
• Responsible, organized, and able to perform repetitive tasks with consistency

Nice-to-have skills

• Previous experience in the FMCG industry

RESPONSIBILITIES

• Identify products from the catalog in retail shelf images
• Annotate and label products with the correct names and attributes
• Analyze incoming annotation requests and ensure annotation quality
• Support the preparation of high-quality datasets for Computer Vision model training