AI Developer - Senior - EY GDS

The opportunity

We are the only professional services organization who has a separate business dedicated exclusively to the financial services marketplace. Join Digital Engineering Team and you will work with multi-disciplinary teams from around the world to deliver a global perspective. Through diverse experiences, world-class learning and individually tailored coaching you will experience ongoing professional development.

We’re looking for an AI Developer with hands-on expertise in AI, machine learning, LLM-based solutions, RAG systems and cloud-native development to join our EY-Advisory-DE Team. This is a fantastic opportunity to be part of a leading firm whilst being instrumental in the growth of a new service offering.

EY GDS Digital Engineering is a unique, industry-focused business unit that provides a broad range of integrated services that leverage deep industry experience with strong functional capability and product knowledge. The Digital Engineering practice works with clients to analyse, formulate, design, mobilize and drive digital transformation initiatives.

Your key responsibilities

Design, develop and implement AI/ML-powered solutions using LLMs, RAG architectures and prompt engineering best practices.

Build Python-based services, APIs and automation components to support scalable AI applications.

Integrate Azure OpenAI, Mistral, Qwen and other model providers into enterprise-grade solutions.

Design and implement agentic AI workflows using frameworks such as Auto-Gen and LangGraph.

Develop, train, validate and maintain machine learning/deep learning models using Scikit-learn, TensorFlow, Keras and N-BEATS where applicable.

Integrate data storage and retrieval capabilities using Azure Blob Storage, CosmosDB, MongoDB/NoSQL concepts, and SQL.

Deploy serverless and containerized components using Azure Functions, Docker and cloud-native engineering practices.

Create unit tests, maintain code quality, use GitHub for version control, and support delivery through modern development tooling.

Apply secure engineering practices including Linux server hardening and operational readiness considerations.

Collaborate with distributed teams, communicate technical decisions clearly and contribute to reusable delivery assets.

Skills and attributes for success

Technical attributes:

AI & ML: LLMs, Azure OpenAI, Mistral, Qwen, RAG Systems, Prompt Engineering, Agentic AI (Auto-Gen, LangGraph), N-BEATS, Scikit-learn, TensorFlow and Keras.

Cloud & Databases: Python (Expert), Azure Blob Storage, CosmosDB and MongoDB (NoSQL notions), SQL and Azure Functions.