Requirements

Expected technologies

Python

R

Our requirements

• Master’s or Ph.D. degree in Computer Science, Information Technology, Data Science, Artificial Intelligence or a related field or Bachelor’s with minimum 5 years experience
• Proven experience as an AI Engineer, Machine Learning Engineer, or similar role
• Experience in developing and deploying machine learning models and AI systems
• Proficiency in programming languages such as Python or R
• Strong knowledge of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn)
• Experience with natural language processing (NLP), computer vision, or other AI techniques
• Familiarity with big data technologies (Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud)
• Strong analytical skills and the ability to work with complex datasets.
• Excellent problem-solving skills with a focus on developing innovative AI solutions

Your responsibilities

• Lead the design and development of innovative machine learning models and algorithms in crossfunctional teams to effectively address specific business challenges
• Lead the implementation of scalable AI products, ensuring alignment with user requirements and strategic business objectives
• Leverage state-of-the-art cloud technologies (e.g., AWS, Azure, Google Cloud) to optimize the industrialization of machine learning models and AI solutions for production deployment
• Provide expert operational support and guidance for machine learning models and algorithms, ensuring their reliability and performance in production environments
• Lead the architecture and design of robust, scalable data pipelines in collaboration with data engineers, cloud engineers, and data scientists, enhancing data access and processing capabilities
• Lead data preprocessing, ingestion, and transformation activities across hybrid environments (both on-premises and cloud), ensuring adherence to best practices and organizational standards
• Establish and promote advanced data quality assurance processes, including validation checks and proactive data drift detection mechanisms, to ensure data consistency and integrity
• Mentor and guide team members in the model training process, focusing on industrialization through cloud technologies and achieving accuracy, reliability through experimentation and iterative improvements
• Lead the evaluation and tuning of models, utilizing metrics and performance benchmarks to refine model parameters and enhance predictive capabilities
• Lead the integration of AI models into existing software systems and workflows, ensuring seamless functionality and optimal user experience.