EY is seeking a Senior Manager to join our dynamic Risk Transformation team. This role offers a unique opportunity to build a career at a global scale, driving innovation and client success. You will play a pivotal role in shaping our clients' risk functions, aligning them with strategic business objectives, and leveraging cutting-edge technology for scalable governance.

As a key contributor, you will be instrumental in business development, crafting sophisticated client solutions, and overseeing large-scale risk transformation initiatives across diverse industries. This position demands robust consulting acumen, exceptional stakeholder management capabilities, and expertise in risk domains and emerging risk technologies.

Lead and execute complex risk transformation engagements, including Internal Audit, Internal Controls (SOX/IFC), Third Party Risk Management (TPRM), and Enterprise Risk Management (ERM).

Collaborate closely with clients and EY regional teams to understand business challenges, define transformation roadmaps, and ensure successful project delivery with a focus on quality, timelines, and outcomes.

Drive business development efforts by leading proposals, RFPs, and strategic pursuits, identifying opportunities within global accounts, and fostering client relationships.

Contribute to the design and development of innovative risk solutions, integrating AI, analytics, automation, GRC platforms, and digital risk solutions, while staying abreast of emerging trends.

Lead, mentor, and develop high-performing teams, supporting their growth and fostering a collaborative, quality-driven culture.

Demonstrated deep expertise in AI Engineering, including Generative AI programming, prompt engineering, context refinement, and experience with models like RNN, CNN, RAG, LLM, and SLM.

Proficiency in AI concepts such as context management, chunking, embeddings, vector databases, agentic AI, agent orchestration, AI architectures, and design patterns is essential.

Experience in creating AI pipelines, data preprocessing, feature engineering, and inference pipelines, alongside a strong understanding of AI governance principles and responsible AI.

Required technical skills include Python programming with AI packages (Keras/TensorFlow), large-scale data handling (Pyspark), agentic AI packages (Langchain/Langgraph or Autogen), cloud deployment (Azure/AWS/GCP), CI/CD implementation, Git, ML Ops, AI model ops, and database experience (SQL/NoSQL).

Secondary skills encompass any of the following risk domains: Enterprise Risk, Operational Risk, Internal Controls, Internal Audit, or Third Party Risk Management, along with strong commercial acumen and experience leading engagements and managing clients and teams.