Your impact on our goals
Model Development: Design, develop, and deploy machine learning and AI models that address specific business needs, such as customer retention, personalized marketing, and operational automation.
Data Preprocessing: Work with Data Engineers to ensure that data is properly processed, cleaned, and transformed for AI model training and testing.
Model Optimization: Continuously monitor and optimize AI models in production to ensure they meet performance targets and deliver expected business value.
AI Pipeline Management: Design and maintain scalable and efficient AI pipelines, ensuring that models are deployed and maintained across all OpCos with minimal disruptions.
Collaboration: Work closely with Data Scientists, Business Analysts, and Solution Architects to ensure that AI models align with business objectives and integrate smoothly into existing workflows.
AI Tooling and Infrastructure: Identify and implement AI tools and frameworks that improve efficiency and scalability of AI model development and deployment (e.g., TensorFlow, PyTorch, scikit-learn, cloud-based AI services).
Research and Innovation: Stay updated on the latest developments in AI, machine learning, and related technologies. Bring new ideas and innovations to the team that can be applied to the Group's AI initiatives.
Deployment & MLOps: Collaborate with ML Ops Engineers to ensure that AI models are deployed reliably and can be scaled across multiple regions (OpCos). Develop and follow best practices for AI model versioning, monitoring, and updates.
Troubleshooting: Diagnose and resolve issues in AI models and pipelines, ensuring that solutions continue to perform in changing business and technical environments.
Experience
Experience in the telecommunications or mobile technology industry.
Knowledge of ML Ops tools and techniques for version control, continuous integration, and deployment of AI models.
Strong understanding of distributed computing and cloud infrastructure.
Experience with reinforcement learning or advanced machine learning techniques.
Strong understanding of machine learning algorithms, deep learning architectures, and model optimization techniques.
Proven ability to build, test, deploy, and maintain AI models at scale, ensuring they meet both technical and business requirements.
Excellent collaboration skills to work closely with Data Scientists, Business Analysts, and the wider technology team.
Demonstrated ability to diagnose and resolve issues in machine learning models and production pipelines.
Experience with data wrangling, preprocessing, and feature engineering techniques.
Qualifications
Bachelor's degree in computer science, Engineering, or related field (Master's degree in AI, Machine Learning, or Data Science preferred).
5+ years of experience in AI or machine learning engineering roles, including hands-on experience with developing and deploying machine learning models.
Strong proficiency in Python and machine learning libraries such as TensorFlow, PyTorch, and scikit-learn.
Experience with cloud-based AI platforms (e.g., AWS, Google Cloud, Azure AI).
Familiarity with data engineering processes and data pipeline development.