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About the Next Frontier
Applied Innovation's Next Frontier focuses on emerging technologies, including Physical AI, Quantum Computing, and Next-Generation AI amongst others. This initiative translates advanced capabilities into measurable outcomes, helping clients and EY teams improve decision-making, strengthen resilience, and unlock new opportunities through technology-driven solutions.
About the Delivery Team
The Next-Generation AI team helps EY teams turn emerging AI capabilities into practical, secure, and governable solutions. The team builds small, high-fidelity experiments to test what AI can realistically do before decisions on scale or production are made. We work closely with EY teams to translate real business and engineering pain points into rapid, value-focused prototypes.
Position Summary
NextGen AI Manager – Technical Innovation, for the Next-Gen AI team provides technical expertise to assess emerging AI capabilities and explore their application to business and client challenges. Drawing on prior hands-on experience designing and building AI/ML solutions, the Manager evaluates relevant technologies, models and architectures and translates technical findings into practical experimentation opportunities. The role works with account teams to understand client problems, identify potential Frontier AI applications, and help shape focused prototypes and experiments. The Manager provides technical guidance to engineering teams, reviews technology choices, and surfaces feasibility considerations, risks and trade-offs during experimentation. The role supports Next-Gen AI leadership with technical insights and recommendations to inform decisions on which opportunities should be explored further.
Essential Responsibilities of the Job:
Emerging AI Technology Scouting & Assessment
- Maintain a strong understanding of developments across next-generation AI, including emerging models, agent architectures, frameworks, open-source technologies, infrastructure patterns, and adjacent technologies.
- Conduct hands-on technical assessments of emerging capabilities to understand their maturity, limitations, enterprise applicability, and potential areas of value.
- Distinguish meaningful technical advances from market hype and summarize implications for the Next-Gen AI team.
- Bring technically grounded insights and recommendations to leadership on areas that may warrant further exploration.
Technical Opportunity Identification
- Review business and client challenges surfaced by account teams and assess whether emerging AI capabilities could offer a relevant or differentiated approach.
- Identify connections between evolving technology capabilities and problems where traditional or currently available approaches may be insufficient.
- Develop initial technical hypotheses for how promising capabilities could be explored through focused experiments or prototypes.
- Provide leadership with technical perspectives on feasibility, maturity, dependencies, and potential value before decisions are made to pursue an opportunity.
Experiment & Prototype Technical Shaping
- Help translate selected opportunities into technically meaningful experiments with clear hypotheses and evaluation criteria.
- Recommend appropriate models, architectures, frameworks, data approaches, and technology components for experimentation.
- Work closely with engineering teams to provide technical direction, review proposed approaches, and challenge design decisions where appropriate.
- Identify technical risks, constraints, and trade-offs early and recommend practical alternatives.
Technical Evaluation & Learning
- Define approaches to evaluate experiments across dimensions such as capability, accuracy, reliability, performance, cost, security, and enterprise readiness.
- Analyze experiment outcomes and determine what the results indicate about the underlying technology and its applicability.
- Capture technical learnings, reusable patterns, and limitations to inform future experimentation across the portfolio.
- Provide evidence-based recommendations to leadership on whether a capability should be explored further, refined, monitored, or deprioritized.
Technical Ecosystem Engagement
- Engage with engineers, technology partners, startups, open-source communities, and EY technical specialists to remain close to emerging developments.
- Assess technology and alliance capabilities from a technical perspective and identify where external expertise or platforms could accelerate experimentation.
- Connect relevant technical expertise into experiments when specialist capabilities are required.
Analytical/Decision Making Responsibilities:
- Examine new AI methods and delivery patterns to determine where they are feasible, responsible, and worth piloting.
- Apply governance, security, and compliance requirements consistently across the experiment-to-delivery lifecycle, ensuring alignment with EY processes.
- Use evidence and outcome signals to make timely trade-offs, re-prioritize work, and steer teams toward the highest-impact results in a fast-moving environment.
Knowledge and Skills Requirements:
- Background in AI/ML, software engineering, data engineering, or solution architecture.
- Prior hands-on experience building AI/ML and GenAI solutions.
- Experience assessing emerging AI technologies and working with engineering teams.
- Tools & stack you may touch:
- Programming: Python, SQL
- AI Platforms: Azure OpenAI / Azure AI Foundry, OpenAI API, etc.
- AI Frameworks: LangChain, LangGraph, LlamaIndex, Hugging Face
- Retrieval / Vector: Azure AI Search, Pinecone, Weaviate, pgvector
- Knowledge Graphs: Neo4j
- Development: GitHub, Docker, Kubernetes
- ML / Evaluation: MLflow, model evaluation and benchmarking tools
- Collaboration: Teams, SharePoint, Azure DevOps
Education & Experience:
8+ years in AI/ML engineering, software engineering, data engineering, solution architecture, or related technical roles.
Demonstrated prior hands-on experience designing and building AI/ML or GenAI solutions.
Experience evaluating emerging AI technologies, architectures, models, and frameworks for enterprise applicability.
Experience working with engineering teams and providing technical guidance across prototypes, pilots, or production solutions.
Strong understanding of enterprise AI constraints, including privacy, security, governance, integration, scalability, and cost.
Excellent written/verbal communication skills with the ability to engage confidently with technical and senior stakeholders.
