About the Role

Become a Lead AI/Computer Vision Engineer driving the creation of advanced AI solutions that analyze visual content and personalize product recommendations.

Key Responsibilities

• Develop and enhance CNN and transformer-based vision models for image evaluation and scoring.
• Design and implement custom transformer architectures to support product development.
• Build robust pipelines to handle high volumes of image data and produce structured metadata.
• Integrate visual intelligence into applications like search, ranking, and personalization.
• Deploy transformer-based models for tailored product recommendations.
• Lead experimentation processes including A/B testing to optimize recommendation quality and conversion rates.
• Manage and document the complete ML model lifecycle from development to deployment.
• Collaborate with cross-functional teams to lead technical initiatives from concept through execution.
• Ensure adherence to best practices in data handling, model assessment, explainability, and monitoring.

Requirements

• Extensive software engineering experience with over 5 years focused on AI/ML roles.
• In-depth expertise in computer vision methods including CNNs, vision transformers, facial recognition, object detection, image classification, and embeddings.
• Strong background in recommendation systems, collaborative filtering, deep learning personalization, and transformer-based recommendation techniques.
• Proficiency in Python and ML frameworks such as PyTorch and TensorFlow.
• Experience deploying machine learning models at scale using Docker, AWS, GCP, or equivalent platforms.
• Demonstrated leadership in managing cross-functional technical projects from inception to delivery.
• Comprehensive understanding of ML lifecycle best practices including data management, model evaluation, explainability, and observability.
• Master’s degree in Computer Science or a related discipline, preferably with a math or physics emphasis.
• English communication skills at B2 level or above.

Preferred Qualifications

• Practical experience with diffusion models.
• Knowledge of graph neural networks (GNNs).
• Understanding of reinforcement learning concepts.

Our Offer

• International projects with top brands.
• Work with global teams of highly skilled, diverse peers.
• Healthcare benefits.
• Employee financial programs.
• Paid time off and sick leave.
• Upskilling, reskilling and certification courses.
• Unlimited access to the LinkedIn Learning library and 22,000+ courses.
• Global career opportunities.
• Volunteer and community involvement opportunities.
• EPAM Employee Groups.
• Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn.