NextGen AI Technical Manager -Growth&Innovation - Assistant Director - Argentina - EY GDS

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.