Join EY's AI & Data team as a Senior Agentic AI Engineer, instrumental in architecting and launching cutting-edge Agentic AI and GenAI solutions. This role involves close collaboration with leadership and product teams to develop robust Python backend services and APIs that seamlessly integrate AI capabilities into applications and enterprise systems. The primary goal is to engineer scalable, production-ready AI solutions that drive business process automation and deliver tangible value.

At EY, we foster a global culture of inclusivity and support, providing the resources and technology necessary for you to excel. Build a career that's as unique as your perspective, contributing to a better working world for all.

Key responsibilities include designing and developing Agentic AI solutions, translating complex requirements into functional agent architectures and workflows. You will build and refine retrieval-augmented generation (RAG) pipelines, encompassing data ingestion, chunking, embedding, retrieval, and response generation. This role also involves creating and enhancing reusable AI/ML accelerators for diverse business areas, such as customer care and operational automation.

Develop and maintain high-performance RESTful APIs and backend services exposing AI functionalities. Implement asynchronous APIs optimized for LLM and agent workloads, and integrate with enterprise systems via REST APIs and JSON. Ensure API security using industry standards like OAuth 2.0 and JWT. Document APIs with OpenAPI/Swagger and contribute to a culture of clean, tested code through reviews and collaboration with product, data engineering, and domain teams.

We seek a skilled Senior Agentic AI Engineer with a strong command of Python and extensive experience with frameworks like FastAPI, Flask, or Django. Proficiency in Pydantic for data validation and asyncio for asynchronous programming is essential. Experience deploying Python services on ASGI/WSGI servers and a solid understanding of RESTful API design, JSON, OpenAPI/Swagger, and API security are required.

Hands-on experience with retrieval-augmented generation (RAG) and Agentic AI frameworks such as LangChain, LangGraph, Semantic Kernel, or AutoGen is crucial. A foundational knowledge of data structures, algorithms, and at least one major cloud platform (Azure, AWS, GCP) is necessary. A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI, or a related quantitative field, coupled with 5-8 years of software engineering experience (minimum 3 years in Python backend/API development and 2 years in GenAI/Agentic AI) is required. Proven success in deploying AI solutions to production is also key.