Join us as a Chief AI/Computer Vision Engineer leading the development of cutting‑edge AI technologies focused on analyzing visual data and enhancing personalized client recommendations. You will oversee the deployment of computer vision, semantic image interpretation, and behavioral AI into scalable, intelligent platforms. Take this opportunity to drive innovative AI solutions that improve client interactions and deliver significant impact.
Responsibilities
- Develop and refine CNN and transformer‑based vision algorithms for image analysis and scoring
- Create and implement specialized transformer frameworks for product innovation
- Construct reliable pipelines to process large‑scale image datasets and generate organized metadata
- Incorporate visual intelligence into functionalities such as search, ranking, and personalization
- Deploy transformer‑driven models to deliver customized product suggestions
- Lead experimentation including A/B testing to enhance recommendation performance and conversion metrics
- Oversee and document the entire ML model lifecycle from design to deployment
- Coordinate with multidisciplinary teams to direct technical projects from ideation to completion
- Maintain compliance with best practices in data management, model validation, explainability, and system monitoring
Requirements
- Extensive software engineering background with more than 7 years in AI/ML specialties
- Expertise in computer vision techniques including CNNs, vision transformers, facial recognition, object detection, image classification, and embeddings
- Strong experience in recommendation algorithms, collaborative filtering, deep learning personalization, and transformer‑based methods
- Proficiency in Python alongside ML frameworks like PyTorch and TensorFlow
- Experience in scaling machine learning solutions using Docker, AWS, GCP, or similar services
- Proven leadership in managing cross‑functional technical initiatives from start to finish
- Thorough knowledge of ML lifecycle best practices covering data handling, model evaluation, explainability, and observability
- Master’s degree in Computer Science or related field with emphasis on mathematics or physics
- English proficiency at B2 level or higher
Nice to have
- Hands‑on experience with diffusion models
- Familiarity with graph neural networks (GNNs)
- Understanding of reinforcement learning principles
We offer
- Career plan and real growth opportunities
- Unlimited access to LinkedIn learning solutions
- Constant training, mentoring, online corporate courses, eLearning and more
- English classes with a certified teacher
- Support for employee’s initiatives (Algorithms club, toastmasters, agile club and more)
- Enjoyable working environment (Gaming room, napping area, amenities, events, sport teams and more)
- Flexible work schedule and dress codeCollaborate in a multicultural environment and share best practices from around the globe
- Hired directly by EPAM & 100% under payroll
- Law benefits (IMSS, INFONAVIT, 25% vacation bonus)
- Major medical expenses insurance: Life, Major medical expenses with dental & visual coverage (for the employee and direct family members)
- 13 % employee savings fund, capped to the law limit
- Grocery coupons
- 30 days December bonus
- Employee Stock Purchase Plan
- 12 vacations daysOfficial Mexican holidays, plus 5 extra holidays (Maundry Thursday and Friday, November 2nd, December 24th & 31st)
- Monthly non‑taxable amount for the electricity and internet bills
EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting‑edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.