AI Engineer

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

As AI Engineer, you will design and build production-grade AI and agentic AI solutions that solve real business problems for EY's clients. You are the core builder on the delivery team, responsible for AI/ML pipelines, agentic workflows, multi-agent coordination, evaluation frameworks, observability and governance that ensure AI solutions work in production.

The opportunity

You will work across complex client engagements designing and implementing AI/ML and agentic AI solutions end to end. You will work closely with business stakeholders, AI Architect, AI Platform Engineers, and Data Scientists to translate complex business challenges into practical, scalable, and responsible AI solutions through maintainable architectural. You will have the opportunity to influence executive stakeholders and help clients responsibly adopt agentic AI to drive measurable business outcomes.

This job posting relates to an existing vacancy within our organization.

Your key responsibilities

• Design and implement production-grade agentic AI workflows using open frameworks such as LangGraph, CrewAI, proprietary cloud-based agentic platforms, including agent orchestration, AI gateways (i.e. MCP, A2A), observability, controls and guardrails.
• Build and optimise retrieval pipelines for Retrieval Augmented Generation (RAG) and agentic workflows, including chunking strategies, embedding models, vector store integration, and re-ranking to ground agent outputs in accurate, current enterprise data.
• Develop and integrate Generative/Agentic AI solutions using large and small language models (LLMs and SLMs).
• Build evaluation frameworks to measure AI solution quality, consistency, and implement telemetry and monitoring to track model drift, latency, output quality, and cost metrics in production.
• Leverage machine learning/deep learning algorithms and foundational statistical concepts to solve real-world AI/ML problems at scale
• Implement best practices for MLOps, LLMOps, across experimental and production environments, including AI pipeline and microservices deployment into cloud environments, APIs, CI/CD, ensuring high availability and low latency.
• Build AI governance and responsible AI controls for AI systems
• Mentor junior team members and contribute to EY AI assets, accelerators, delivery standards, and go-to-market offerings in AI/ML, agentic and generative AI