Join EY and build a career that's as unique as you are. Leverage your expertise to shape the future of AI by designing, building, and scaling advanced AI systems. This role offers the opportunity to work with cutting-edge technologies like Large Language Models (LLMs), autonomous agents, and multi-agent architectures to drive innovation across enterprise business processes.

As a technical leader, you will architect and deliver production-grade Agentic AI platforms. Your work will involve integrating LLMs with existing enterprise systems and knowledge sources. You will also guide engineering teams, champion AI engineering best practices, and collaborate with business stakeholders to identify key opportunities for AI-driven transformation.

Key responsibilities include designing and implementing enterprise-scale Agentic AI solutions that leverage LLMs, tools, memory, and reasoning frameworks. You will architect Generative AI systems to address complex business challenges and collaborate with team members to deploy AI Agents into production.

Responsibilities also encompass monitoring and managing AI systems to ensure optimal performance and reliability. You will develop and implement best practices for AI system development, deployment, and management, while staying abreast of the latest advancements in AI research and applying them to our projects. Collaboration with engineers, scientists, and product managers is crucial for delivering innovative AI solutions, and you will also mentor junior engineers.

A Master's degree in Computer Science, Artificial Intelligence, or a related field is required. This position demands a minimum of 5 years of experience in Machine Learning and Generative AI, coupled with at least 6 years of experience in software development.

Essential skills include a deep understanding of Agentic AI patterns such as reasoning, planning, reflection, memory, and tool use. Proficiency with modern LLM ecosystems like OpenAI, Azure OpenAI, Anthropic, and Gemini is expected. Experience with advanced RAG, knowledge graphs, vector databases, and semantic search is also highly valued. Familiarity with prompt engineering, ML optimization, and distributed computing is important.