Position Summary
You will be part of our GDS Assurance Digital, helping EY’s sector and service line professionals deliver Microsoft Copilot–based solutions that improve audit quality, reduce cycle times, and drive measurable productivity gains at scale. You will work with multi-disciplinary teams across the region, operating at the intersection of Assurance domain, Generative AI behaviour, Copilot platform engineering, data, security, risk, and global stakeholders.
You will own outcomes end-to-end from translating Assurance use cases into clear conversational experiences, through to building, testing, and governing prompts and agents that are engagement-ready, trusted, and repeatable. You are comfortable proposing pragmatic approaches to balance value, quality, risk and compliance, ensuring solutions are explainable, traceable, and iterated based on evidence from testing and user feedback.
You will build valued relationships with internal stakeholders and contribute to the development of a portfolio of business by focusing on high-impact opportunities. You will lead adoption and change through rollout plans, prompt playbooks, enablement materials, training, and stakeholder communications—using user feedback and usage insights to continuously improve prompt effectiveness and agent design across regions and teams.
Responsibilities and Skills
- Designing, refining, and optimizing prompts (system prompts, grounding instructions, response constraints)
- Engineering multi-turn conversational flows with strong guardrails (role-based prompting, structured reasoning, retrieval-augmented generation (RAG), and hallucination reduction patterns)
- Building and configuring Microsoft Copilot Studio agents (topics, triggers, entities, actions, plugins, variables, and orchestration logic) to deliver coherent end-user experiences
- Integrating agents with enterprise data sources, APIs, workflows, and assurance tools while adhering to security, access control, and platform standards
- Executing rigorous AI testing strategies (prompt validation, negative/edge-case testing, evaluation criteria, and output quality checks) to improve accuracy, consistency, and explainability
- Operating disciplined AI quality, risk and governance practices (documentation of prompt logic, assumptions/limitations, human-in-the-loop controls, and support for certification/QRM and engagement readiness reviews)
Required Experience & Skills
- Excellent academic background, including at a minimum a Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Information Systems, Business Analytics, or a related field with focus on AI-enabled product development and automation.
- 4 to 7 years of overall engineering experience with 2-3 years of experience in Generative AI, with focus on following:
- AI-enabled product roles, automation, or digital transformation delivery
- Hands-on prompt engineering for LLM-based systems
- Building solutions using Microsoft Copilot and/or Copilot Studio, Python, LangGraph
- Understanding of LLM behaviour, limitations, risks, and evaluation techniques
- Translating complex business / regulatory requirements into structured AI interactions (conversation design, intent flows, guardrails)
- Working in Agile delivery models with cross-functional global teams
- Strong technical skills in AI solution design and implementation, including:
- Prompt patterns, tool-use prompting, and interaction design for copilots/agents
- Evaluation approaches (quality, safety/risk, consistency, regressions)
- Orchestrating multi-step interactions (flows, routing, error handling, fallback patterns)
- Integration-oriented thinking for enterprise-grade AI experiences (APIs, services, data grounding)
- Additionally, the following will be considered a strong asset:
- Experience with RAG patterns, vector search, and enterprise data grounding
- Exposure to AI governance, risk management, or model validation frameworks
- Knowledge of Azure services and API-based integrations
- Ability to create conversation flows, prompt maps, and AI interaction diagrams
- Strong written and verbal communication, presentation, stakeholder management, and documentation skills—able to explain AI behaviour clearly to non-technical audiences.
- Strong analytical, problem solving and critical thinking skills.
- Ability to work under tight timelines across multiple parallel deliveries.