Manager / Area Manager Artificial Intelligence

· Strategic AI Vision: Develop and champion the AI vision, identifying and prioritizing opportunities across the TSL value chain. This includes conceptualizing autonomous business and operating models powered by AI and Generative AI technologies. Collaborate with stakeholders to define solution objectives, deliverables, and timelines.

· AI Implementation: Design, development, and deployment of AI solutions, with a specific emphasis on integrating Generative AI capabilities. Direct the AI learning trajectory, proactively identifying and mitigating potential risks.

· Technical Leadership & Partnerships: Collaborate with internal and external stakeholders & partners to build robust AI solutions and ensure seamless implementation.

· Program & Change Management: Lead the end-to-end transformation process, from concept to change management, ensuring successful adoption across business units. Foster collaboration with business verticals to envision and drive the adoption of new operating practices.

· Communication & Stakeholder Engagement: Effectively communicate AI results, insights, and strategic direction to senior management, business users, external partners, and other stakeholders.

Innovation & Continuous Learning: Stay abreast of the latest advancements in AI, including Generative AI, fostering a culture of innovation and continuous learning within the team. Actively scan the external ecosystem for emerging opportunities and manage a robust pipeline of AI initiatives.

· Deep understanding of AI/ML algorithms and techniques, including Generative AI models (e.g., large language models, diffusion models), supervised, unsupervised, and reinforcement learning.

· Expertise in AI architecture design and implementation: Familiarity with cloud platforms (AWS, Azure, GCP), big data technologies (Hadoop, Spark), and AI/ML frameworks (TensorFlow, PyTorch).

· Understanding of frameworks & methods like RAG, Fine-tuning, Pre-training

· Experience with data engineering and data management, including data cleaning, preprocessing, feature engineering, and data pipelines.

· Proficiency in programming languages: Python, R, or similar languages are essential.

· Knowledge of model deployment and monitoring: Experience with MLOps practices and tools for deploying and managing AI models in production environments.

· Understanding of bias mitigation, privacy preservation, and responsible AI practices.

· Familiarity with various AI applications and their business implications, across different functional areas.

· Ability to design scalable, robust, and maintainable AI systems.

Understanding of IT-OT architecture