Join EY's Global Delivery Services as a leader in SAP BTP Applied Intelligence, focusing on advanced AI and Machine Learning capabilities. This role is pivotal in establishing and scaling enterprise-grade AI engineering, including Generative AI, MLOps, LLMOps, and intelligent automation.

Shape the future of AI by building high-performing teams and elevating technical standards. You'll drive the development of cutting-edge AI solutions, fostering innovation and creating sustainable capabilities across predictive, analytical, agentic, and autonomous use cases. This is an exceptional opportunity to lead an AI engineering practice that delivers transformative results.

Lead the advancement of AI engineering disciplines such as machine learning, deep learning, MLOps, and LLMOps on SAP BTP and other cloud platforms.

Establish robust technical standards, engineering frameworks, and best practices for enterprise AI solution development.

Architect and oversee the delivery of diverse AI solutions, including predictive analytics, computer vision, NLP, recommendation systems, intelligent automation, and Generative AI.

Implement scalable processes for model development, deployment, monitoring, and governance.

Drive the adoption of MLOps and LLMOps to enhance reliability, traceability, observability, and operational efficiency.

Mentor and develop AI engineers, guiding them beyond prompt engineering into broader AI engineering domains.

Collaborate with stakeholders to identify high-value AI opportunities and translate them into production-ready solutions.

Evaluate emerging AI frameworks and tools to continually strengthen organizational capabilities.

Lead technical reviews, solution assurance, architecture discussions, and capability-building initiatives.

Demonstrate extensive experience in machine learning, deep learning, statistical modeling, and AI solution engineering.

Possess hands-on expertise with Python and modern AI ecosystems like TensorFlow, PyTorch, Scikit-learn, and frameworks such as LangChain.

Exhibit a deep understanding of model training, optimization, evaluation, deployment, monitoring, and lifecycle management.

Proven experience in establishing MLOps, LLMOps, data science engineering, and AI platform practices.

Knowledge of modern data engineering concepts, feature engineering, model observability, and AI governance is essential.

Showcase excellent client management skills across varied demographics.

Practical experience with Generative AI technologies, foundation models, RAG architectures, AI agents, and orchestration frameworks is required.

Possess a strong understanding of cloud-based AI services and distributed AI workloads.

Ability to effectively balance research-oriented innovation with enterprise delivery demands.

Exhibit excellent communication, presentation, stakeholder management, and consulting skills.

Demonstrated experience leading technical teams and coaching engineers to enhance their capabilities and performance.