AI Architect

At EY, we’re all in to shape your future with confidence. 

We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. 

Shape the future with confidence.

The Opportunity: Your Next Adventure Awaits?

Our AI team delivers production-grade AI systems in various industries & organizations. This cross-sector model gives you broad exposure to different regulatory environments, data landscapes and operating models, while building deep engineering capability that transfers across industries.

Join a team that is building real AI‑powered solutions for international clients in different sectors. You’ll translate business challenges into user stories, implement early features (e.g., LLM/RAG), and learn how to deliver secure, reliable solutions in enterprise settings.

Your Key Responsibilities

• Artificial Intelligence: Designing, building and implementing AI use cases, working with the major vendors, platforms, tools and applications: GPT, Mistral, Azure, CoPilot, Snowflake, Databricks, …;
• AI-Driven Innovation: Explore cutting-edge technologies like Machine Learning, Deep Learning, and GenAI to revolutionize entire organizations;
• Solution design & implementation: Build LLM/RAG features and integrations under guidance by writing clean, tested and documented codes;
• Support discovery workshops: convert needs into user stories and acceptance criteria, contribute to demonstrations and feedback sessions;
• Client-facing delivery: Facilitate discovery workshops and translate identified needs into user stories and well-defined acceptance criteria;
• Data Quality & Governance: Develop strategies for treating data as a critical asset;
• Product mindset & continuous improvement: Contribute to documentation, reusable accelerators and squad playbooks informed by field lessons.

Your Profile

You have at least 4-5 years of experience:

Must-have skills & capabilities

• Strong Python skills for AI/ML development, APIs, and rapid prototyping
• Solid SQL and data engineering fundamentals (ETL/ELT, pipelines, APIs)
• Hands-on experience with GenAI concepts: RAG, prompt engineering, agents, embeddings, vector databases
• Understanding of LLMOps / MLOps basics: deployment, monitoring, evaluation, guardrails
• Cloud platform familiarity (preferably Microsoft Azure, AWS, or Google Cloud)