SAP BTP Applied Intelligence AIML Manager

We are looking for a highly experienced AI and Machine Learning leader to establish and scale advanced AI engineering capabilities beyond Generative AI adoption. This role will focus on building enterprise-grade expertise across machine learning, deep learning, MLOps, LLMOps, and intelligent automation while enabling the development of next-generation AI solutions.

The successful candidate will combine strong hands-on AI engineering experience with leadership capability and a passion for creating high-performing teams. They will be responsible for elevating technical standards, guiding the development of advanced AI solutions, and creating sustainable capability across both traditional AI and emerging Generative AI technologies.

This role offers a unique opportunity to shape an AI engineering practice that can deliver across predictive, analytical, agentic, and autonomous AI use cases.

• Lead the development and growth of advanced AI engineering capabilities across machine learning, deep learning, MLOps, and LLMOps disciplines on SAP BTP and other cloud platforms.
• Define technical standards, engineering frameworks, and best practices for enterprise AI solution development.
• Architect and oversee the delivery of AI solutions spanning predictive analytics, computer vision, NLP, recommendation systems, intelligent automation, and Generative AI.
• Establish scalable model development, deployment, monitoring, and governance processes.
• Drive adoption of MLOps and LLMOps practices to improve reliability, traceability, observability, and operational performance.
• Mentor and develop AI engineers, helping them expand beyond prompt engineering into broader AI engineering disciplines.
• Collaborate with business and technology stakeholders to identify high-value AI opportunities and convert them into production solutions.
• Evaluate emerging AI frameworks, tooling, and platforms to strengthen organisational capabilities.
• Lead technical reviews, solution assurance activities, architecture discussions, and capability-building initiatives.
• Support thought leadership, innovation activities, technical asset development, and market-facing AI initiatives.

• Strong experience in machine learning, deep learning, statistical modelling, and AI solution engineering.
• Hands-on expertise with Python and modern AI ecosystems including TensorFlow, PyTorch, Scikit-learn, LangChain, LangGraph, or comparable frameworks.
• Deep understanding of model training, optimisation, evaluation, deployment, monitoring, and lifecycle management.
• Experience establishing MLOps, LLMOps, data science engineering, and AI platform practices.
• Knowledge of modern data engineering concepts, feature engineering, model observability, and AI governance.
• Excellent client management experience across multiple and varied demographical contexts.
• Practical experience with Generative AI technologies, foundation models, RAG architectures, AI agents, and orchestration frameworks.
• Strong understanding of cloud-based AI services and distributed AI workloads.
• Ability to balance research-oriented innovation with enterprise delivery requirements.
• Excellent communication, presentation, stakeholder management, and consulting skills.
• Demonstrated experience leading technical teams and coaching engineers toward higher levels of capability and performance.

• Experience building and managing AI engineering teams or centres of excellence.
• Exposure to large-scale AI programmes across multiple industries.
• Experience with model evaluation frameworks, AI risk management, Responsible AI, and AI governance.
• Familiarity with knowledge graphs, multi-agent systems, reinforcement learning, or advanced deep learning techniques.
• SAP BTP, Azure, AWS, Google Cloud, AI/ML, Data Science, or MLOps certifications.
• Contributions to AI communities, publications, open-source projects, patents, or recognised thought leadership.