Responsibilities :
1. Research and develop machine learning models for predictive analytics and process optimization.
2. Design and train deep neural networks and other AI models for pattern recognition, natural language processing, computer vision, among others.
3. Deploy AI systems in production, ensuring scalability, robustness, and maintainability of the solutions.
4. Collaborate with interdisciplinary teams to integrate AI capabilities into existing products and services.
5. Optimize algorithms and models to improve uk telegram data accuracy and performance across different contexts and data sets.
6. Perform rigorous testing and evaluation to validate and improve the quality of AI models.
7. Stay up to date with advances in the AI field and apply new techniques and methodologies as needed.
8. Document processes, results, and technical decisions to facilitate collaboration and learning within the team.
9. Collaborate with data scientists and developers to design efficient data pipelines and scalable production systems.
10. Participate in the training and professional development of other team members in AI techniques and best practices.
Requirements :
Previous experience developing AI and machine learning solutions in enterprise environments.
Strong knowledge of programming languages such as Python and library tools such as TensorFlow, PyTorch, scikit-learn, among others.
Demonstrated ability to translate business requirements into effective AI-based technical solutions.
Analytical skills and ability to work with large volumes of data.
Excellent communication skills and ability to collaborate effectively in multidisciplinary teams.
How would you communicate complex technical outcomes to non-technical stakeholders?
Description : Assesses the candidate’s communication skills and ability to translate technical concepts into understandable terms for non-specialist audiences.Sample answer:“I would use clear visualizations and practical examples to explain the impact and benefits of AI models in terms of business metrics such as ROI or improved accuracy. I would avoid using technical jargon and make sure to focus on tangible outcomes and practical applications of the proposed solutions.”
