New opportunity

Learning Advisory Associate Manager

Accenture · Gurugram, India

About this role

The Learning Advisory Associate Manager will act as a subject matter expert responsible for designing, developing, and delivering high-quality learning solutions focused on Large Language Models (LLMs), Generative AI, Prompt Engineering, and related AI technologies. This role involves enabling learners through instructor-led virtual and blended learning experiences, creating engaging content, assessments, hands-on labs, and certification programs. Responsibilities include translating complex technical designs into structured learning components, collaborating with instructional designers and subject matter experts, and delivering training sessions on topics such as model architectures, embeddings, semantic search, vector databases, and responsible AI principles. The candidate will also develop reusable code samples, prompt libraries, and practical exercises while mentoring junior trainers and presenting learning insights to senior stakeholders. Requirements include 10 to 14 years of experience and a graduation degree. Candidates must possess deep technical expertise in Machine Learning, Deep Learning, RAG, Agentic AI, and orchestration frameworks like LangChain and LangGraph. Proficiency in fine-tuning methodologies (LoRA, QLoRA, PEFT) and vector databases (Pinecone, Weaviate, Chroma) is essential. The role requires the ability to work with minimal supervision and may involve rotational shifts.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Large Language ModelsGenerative AIPrompt EngineeringMachine LearningDeep LearningRetrieval Augmented GenerationAgentic AIArtificial Intelligence Orchestration FrameworksLangChainLangGraphLoRAQLoRAPEFTPineconeWeaviateChromaResponsible AIAI EthicsModel EvaluationInstructor-Led Training

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Technical Curriculum Design for Generative AI
The role requires translating complex technical concepts like RAG, agentic AI, and vector databases into structured, high-quality learning assets. You must demonstrate the ability to create hands-on labs, coding exercises, and assessments that effectively bridge the gap between theoretical AI architecture and practical enterprise application.
Subject Matter Expertise in AI Orchestration
You must act as a recognized authority on LLMs, LangChain, LangGraph, and fine-tuning methodologies (LoRA, QLoRA). The priority is to maintain deep technical fluency to validate content accuracy and mentor junior trainers, ensuring all training materials reflect current industry standards and responsible AI principles.
Stakeholder Engagement and Strategic Reporting
Beyond delivery, you are expected to present learning insights and program updates to senior stakeholders. This involves articulating the impact of training programs on capability development and driving continuous improvement in learner engagement and assessment effectiveness.

Likely challenges to think through

Balancing Technical Depth with Pedagogical Clarity
A significant challenge is simplifying highly technical topics like semantic search or model evaluation for diverse learner groups without losing technical rigor. Prepare by practicing how you explain complex AI workflows to non-experts while maintaining the depth required for technical practitioners.
Rapid Obsolescence of AI Content
The field of Generative AI evolves weekly, making static training materials quickly outdated. You will face the challenge of continuously updating content and labs. Prepare by demonstrating a systematic approach to monitoring AI research and integrating new developments into existing curriculum frameworks.

Practice your answers

Technical practice

RAG Architecture and Implementation

What to coverExplain the core components of a Retrieval-Augmented Generation (RAG) pipeline. Discuss how you would teach the trade-offs between different embedding models and vector database selection (e.g., Pinecone vs. Chroma) to ensure high-quality retrieval.

Orchestration Frameworks: LangChain vs. LangGraph

What to coverDescribe the functional differences between LangChain and LangGraph for building agentic AI workflows. Suggestion: Prepare to explain how you would structure a hands-on lab to demonstrate state management and cyclic graphs in LangGraph.

Fine-Tuning Methodologies (LoRA/QLoRA/PEFT)

What to coverCompare Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA and QLoRA against full model fine-tuning. Discuss the technical constraints and resource requirements you would highlight when training learners on these methods.

Prompt Engineering Best Practices

What to coverHow do you translate complex prompt engineering techniques, such as Chain-of-Thought or Few-Shot prompting, into structured, repeatable learning modules? Focus on how to measure the effectiveness of these prompts through scenario-based assessments.

Responsible AI and Ethics in Training

What to coverWhen designing curriculum for Generative AI, how do you integrate Responsible AI principles? Suggestion: Be ready to discuss how to teach learners to identify and mitigate model bias, hallucinations, and data privacy risks.

Vector Database Fundamentals

What to coverExplain the role of vector databases in semantic search and how they differ from traditional relational databases. Detail how you would explain the concept of vector embeddings and indexing strategies to a technical audience.

Model Evaluation and Benchmarking

What to coverWhat frameworks or metrics would you use to evaluate the performance of an LLM-based application? Discuss how to teach learners to design evaluation frameworks that go beyond simple accuracy metrics to include relevance and safety.

Translating Technical Concepts for Learners

What to coverDescribe your process for breaking down complex model architectures into digestible learning components. Suggestion: Provide an example of how you would simplify a high-level technical concept like 'Attention Mechanisms' for a blended learning session.

Behavioural practice

Managing Stakeholder Expectations

What to coverDescribe a situation where you had to present learning insights or curriculum recommendations to senior stakeholders. How did you ensure your technical recommendations were aligned with business objectives?

Mentoring and Capability Building

What to coverProvide an example of how you have mentored junior trainers or subject matter experts to improve their delivery of technical content. Focus on the specific actions you took to support their professional growth.

Adapting to Rapid Technological Change

What to coverThe AI field evolves quickly; describe a time you had to rapidly update a training program due to a new technological advancement. How did you ensure the accuracy and relevance of the new material?

Collaborating with Instructional Designers

What to coverDescribe a project where you collaborated with instructional designers to convert complex technical designs into a structured learning experience. How did you resolve disagreements regarding technical depth versus learner accessibility?

Sharpen your knowledge

Topics to prepare

Retrieval Augmented Generation (RAG) Architectures
Understand the end-to-end flow of RAG, including document ingestion, chunking strategies, embedding models, and vector search retrieval. Exercise: Create a conceptual diagram mapping how a user query interacts with a vector database to augment an LLM response, specifically identifying where latency and accuracy bottlenecks occur.
Responsible AI and Ethical Frameworks
Study principles of fairness, transparency, and bias mitigation in AI models. Exercise: Draft a checklist for evaluating an LLM application for potential ethical risks, such as hallucination or data privacy leaks, to be used as a module in a training curriculum.
Agentic AI and Orchestration
Conceptualize how autonomous agents use tools and reasoning loops to complete tasks. Exercise: Outline a lesson plan that explains the difference between a standard LLM chain and an agentic workflow using LangGraph, focusing on state management and tool execution.

Skills to revise

Orchestration with LangChain and LangGraph
Develop proficiency in building complex workflows. Exercise: Write a Python script using LangGraph to create a multi-step agent that retrieves information from a vector store and summarizes it, ensuring the code is modular and suitable for a hands-on lab environment.
Fine-Tuning Methodologies (LoRA/QLoRA/PEFT)
Master parameter-efficient fine-tuning techniques. Exercise: Prepare a technical demonstration or code sample that compares the memory footprint and performance of a base model versus a model fine-tuned using QLoRA.
Vector Database Implementation
Gain hands-on experience with Pinecone, Weaviate, or Chroma. Exercise: Build a small-scale prototype that indexes a set of technical documents into a vector database and performs a semantic search, documenting the configuration steps for a learner guide.

Your preparation checklist

Develop a Generative AI Training Module
Create a sample lesson plan or storyboard that explains a complex concept like RAG or Agentic AI. Include a hands-on lab exercise, a set of assessment questions, and a brief facilitator guide to demonstrate your ability to translate technical designs into structured learning components.
Prepare a Technical Portfolio of AI Assets
Compile a collection of reusable code samples, prompt libraries, or Jupyter notebooks that demonstrate your proficiency in LangChain, LangGraph, and vector database integration (e.g., Pinecone or Chroma). Ensure these assets are documented clearly for a learner audience.
Articulate Responsible AI and Evaluation Frameworks
Draft a brief presentation or white paper outlining your approach to teaching Responsible AI principles and model benchmarking. Be prepared to discuss how you would integrate ethics into a technical curriculum and how you measure the effectiveness of AI training programs.
Formulate Strategic Questions for Stakeholders
Prepare high-level questions regarding the current maturity of Accenture's AI learning programs, the specific technical challenges learners face with orchestration frameworks, and how the team balances rapid technological advancements with the need for stable, long-term certification frameworks.

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