Learning Advisory Associate Manager

What matters most

• Technical SME for Google Generative AI Ecosystem

  The role requires deep expertise in Vertex AI, Generative AI Studio, and the Gemini model family. You must be prepared to demonstrate hands-on proficiency in building and evaluating applications, specifically using Python and Google AI SDKs to implement prompt engineering techniques like chain-of-thought and structured output.

• Curriculum Design and Delivery

  You are responsible for creating and delivering technically rigorous training, including instructor-led sessions, hands-on labs, and certification content. Success depends on your ability to translate complex AI concepts into accessible, learner-centered educational material that aligns with Google Cloud AI program standards.

• Strategic Stakeholder and Partnership Management

  You must act as a bridge between Accenture's internal capability leads and Google partnership teams. This involves presenting training roadmaps to senior stakeholders, ensuring alignment with business goals, and actively participating in Google Cloud partner learning communities to represent Accenture's interests.

Likely challenges to think through

• Balancing Technical Depth with Instructional Clarity

  The challenge lies in maintaining high-level technical accuracy for advanced topics like RAG implementation and MLOps while ensuring the content remains digestible for diverse learner levels. Prepare by practicing how you explain complex technical trade-offs in AI model deployment to non-technical stakeholders.

• Rapid Evolution of Generative AI Tools

  Given the fast-paced nature of Google's Generative AI ecosystem, you will face the difficulty of keeping training materials current with frequent SDK updates and new model releases. Prepare by demonstrating your methodology for continuous self-learning and how you rapidly integrate new technical features into existing curriculum.

• Navigating Responsible AI and Safety Standards

  Implementing enterprise-grade AI requires strict adherence to Responsible AI principles. You may face difficulty in designing training that effectively teaches safety evaluation techniques without stifling innovation. Prepare by articulating how you integrate safety guardrails into the development lifecycle of AI agents.

Topics to prepare

• Generative AI Lifecycle and Evaluation

  Focus on the end-to-end process of building applications using Vertex AI. Exercise: Outline a curriculum module that explains how to evaluate Gemini model outputs using grounding and safety metrics. This is relevant because the role requires acting as an SME for building and evaluating generative AI applications.

• Instructional Design for Technical Training

  Focus on translating complex AI concepts into learner-centered training. Exercise: Create a lesson plan structure for a hands-on lab involving Vertex AI Agent Builder. This is relevant as the role involves designing instructor-led training and developing certification content.

• Responsible AI and Safety Principles

  Focus on Google's specific safety evaluation techniques for generative models. Exercise: Review Google's Responsible AI guidelines and draft a checklist for assessing potential biases in a RAG-based application. This is relevant as the role requires deep knowledge of safety evaluation techniques.

Skills to revise

• Prompt Engineering Techniques

  Master zero-shot, few-shot, chain-of-thought, and structured output prompting. Exercise: Use Google Generative AI Studio to iterate on a prompt for a specific business use case, comparing the output quality across different techniques. This is a core requirement for the role.

• Python SDK for Google Cloud AI

  Develop proficiency in using the Vertex AI SDK for Python. Exercise: Write a script that programmatically calls a Gemini model via the Vertex AI SDK to perform a text summarization task. This is essential for delivering technical training on AI SDKs.

• RAG Implementation on Vertex AI

  Understand the integration of Vector Search and Matching Engine. Exercise: Build a small-scale prototype that uses Vertex AI Vector Search to retrieve context for a Gemini model. This is a preferred qualification that demonstrates advanced technical capability.

• Demonstrate Vertex AI and Gemini Proficiency

  Prepare a technical walkthrough of a generative AI application you built using Vertex AI and Gemini. Highlight your specific use of Generative AI Studio for prompt design, grounding, and evaluation techniques to show hands-on expertise.

• Showcase Instructional Design for Technical Content

  Create a sample outline for a 60-minute training module on a complex topic like RAG implementation or MLOps on GCP. Include learning objectives, a hands-on lab exercise, and a method for assessing learner competency.

• Prepare Evidence of Prompt Engineering Mastery

  Document specific examples where you applied zero-shot, few-shot, and chain-of-thought prompting to improve model output. Be ready to explain how you structured these prompts to meet specific business requirements.

• Develop Stakeholder Engagement Scenarios

  Prepare to discuss how you have previously aligned technical training programs with business goals. Draft a brief narrative on how you would present a new AI curriculum roadmap to senior stakeholders to gain their buy-in.

• Formulate Strategic Questions for the Interviewer

  Prepare questions regarding the current priorities of the Google partnership team, the primary challenges in scaling AI training across the organization, and how the team currently integrates Responsible AI principles into their curriculum.