New opportunity

AI Copilot Developer

Accenture · Bengaluru, India

About this role

Accenture is seeking an innovative AI Copilot Developer to join the Strategy & Consulting Global Network. This role focuses on designing, building, and maintaining AI-powered solutions within the Microsoft Copilot ecosystem to drive business growth and innovation for clients across various industries.

Key Responsibilities:

Design, develop, and deploy custom Copilot solutions across Microsoft 365 applications including Power BI, Teams, Outlook, SharePoint, Excel, and PowerPoint.
Utilize Microsoft Copilot Studio to create conversational AI experiences and intelligent agents.
Integrate Copilot capabilities with business systems using REST APIs, custom connectors, and Microsoft Graph API.
Build automation workflows using Power Automate.
Collaborate with stakeholders to translate business needs into technical specifications.
Conduct testing, validation, and monitoring of AI agents to ensure performance and security compliance.

Required Qualifications

Bachelor’s or master’s degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
At least 2 years of proven experience in software development with a focus on Microsoft AI, Power BI, Power Platform, and Copilot Studio.
Strong understanding of generative AI, LLMs, and prompt engineering.
Proficiency in Python or JavaScript/TypeScript.
Hands-on experience with Azure AI services and cloud computing (AWS, Azure, or GCP).
Solid understanding of AI algorithms, machine learning, NLP, computer vision, and deep learning.
Excellent communication skills for articulating technical concepts to non-technical stakeholders.

Preferred Qualifications

Experience in a client-facing or technical consultancy role.
Familiarity with Agile/Scrum methodologies.
Relevant Microsoft certifications in AI, Azure, or Microsoft 365.
Experience with GitHub Copilot.

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

Skills for this role

Microsoft Copilot StudioGenerative AIMicrosoft Power PlatformPower BIPower AutomateMicrosoft 365REST APIsMicrosoft Graph APIPythonJavaScriptTypeScriptAzure OpenAILarge Language Models (LLMs)Prompt EngineeringMachine LearningNatural Language Processing (NLP)Computer VisionDeep LearningCloud Computing

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

Custom Copilot Development and Integration
The core of the role involves designing and deploying AI agents within the Microsoft 365 ecosystem. You must demonstrate proficiency in using Copilot Studio and integrating these solutions with business systems via REST APIs, custom connectors, and the Microsoft Graph API to ensure seamless functionality across applications like Teams, Outlook, and Power BI.
Translating Business Needs into Technical AI Strategy
As a consultant, you are expected to bridge the gap between non-technical stakeholders and complex AI implementations. You must be able to assess client business processes, identify high-impact use cases, and translate these requirements into actionable technical specifications and AI strategy frameworks.
Governance, Security, and Compliance
Beyond development, you are responsible for the lifecycle of AI agents, including rigorous testing, validation, and monitoring. You must ensure that all deployed solutions adhere to enterprise-grade security and compliance standards, which is critical when handling client data within the Microsoft 365 environment.

Likely challenges to think through

Balancing Technical Complexity with User Adoption
A significant challenge is ensuring that advanced AI solutions remain intuitive for end-users. Prepare by focusing on how you document and train non-technical users to adopt new Copilot features, ensuring that the technical sophistication of your agents does not hinder usability.
Managing Rapidly Evolving AI Ecosystems
The Microsoft Copilot and LLM landscape changes quickly. You will face the challenge of maintaining current knowledge while delivering stable, production-ready solutions. Prepare by discussing your methodology for staying updated on new AI advancements and how you decide when to integrate new features versus maintaining existing, stable workflows.
Navigating Multi-Stakeholder Consultative Environments
Working in a global consultancy requires managing diverse expectations across different industries. You may face conflicting requirements from stakeholders. Prepare by highlighting your experience with Agile/Scrum methodologies and your ability to articulate technical trade-offs clearly to ensure project alignment with client goals.

Practice your answers

Technical practice

Designing Custom Copilot Solutions

What to coverExplain your approach to designing a custom Copilot agent within the Microsoft 365 ecosystem. Discuss how you determine the appropriate scope, select data sources, and ensure the agent aligns with specific business workflows in applications like Teams or SharePoint.

Integrating Copilot with Business Systems

What to coverDescribe the process of integrating Copilot with external business systems using REST APIs and the Microsoft Graph API. Focus on how you handle authentication, data mapping, and ensuring secure data retrieval for the AI agent.

Prompt Engineering Best Practices

What to coverDiscuss your methodology for prompt engineering to improve the accuracy and relevance of LLM responses. Explain how you handle system instructions, context window limitations, and techniques to mitigate hallucinations in a professional setting.

Leveraging Microsoft Copilot Studio

What to coverDetail your experience using Microsoft Copilot Studio to build conversational AI experiences. Explain how you manage topics, entities, and fallback actions to create a robust and user-friendly intelligent agent.

Automation with Power Automate

What to coverProvide an example of how you have used Power Automate to extend the functionality of an AI solution. Focus on how you trigger workflows based on user interactions with a Copilot agent to perform downstream tasks.

AI Model Selection and Evaluation

What to coverWhen tasked with choosing an LLM for a specific client use case, what criteria do you use? Explain your process for evaluating model performance, including validation techniques and monitoring for drift or accuracy issues post-deployment.

Data Security and Governance

What to coverHow do you ensure that AI solutions built on the Microsoft Power Platform comply with enterprise-grade security and governance standards? Address topics like data residency, access control, and preventing unauthorized data leakage through the AI interface.

Python/TypeScript for AI Development

What to coverCompare the use of Python versus TypeScript when building custom connectors or middleware for AI applications. Explain which language you prefer for specific tasks like data transformation or API integration and why.

Behavioural practice

Translating Technical Concepts for Stakeholders

What to coverDescribe a situation where you had to explain a complex AI or technical strategy to a non-technical stakeholder. How did you adjust your communication style to ensure they understood the business value and risks?

Handling Ambiguous Business Requirements

What to coverTell me about a time when you were tasked with building a solution based on vague business requirements. What steps did you take to clarify the needs and translate them into actionable technical specifications?

Collaborating in Cross-Functional Teams

What to coverProvide an example of a successful collaboration with a cross-functional team to deliver an AI project. How did you manage differing priorities or perspectives to ensure the project met its objectives?

Managing Project Challenges in an Agile Environment

What to coverDescribe a time when a project faced unexpected technical hurdles or scope changes during an Agile sprint. How did you adapt your development plan to maintain progress while meeting the client's core needs?

Sharpen your knowledge

Topics to prepare

Generative AI and LLM Lifecycle
Review the end-to-end lifecycle of LLM-based applications, specifically focusing on problem definition, data preparation, model selection, validation, and monitoring. Exercise: Map out a hypothetical project lifecycle for deploying a custom Copilot agent, identifying key performance metrics and security compliance checkpoints at each stage.
Microsoft Copilot Ecosystem Architecture
Understand how Copilot integrates across Microsoft 365, Power Platform, and Azure AI services. Exercise: Create a conceptual diagram showing how a user query in Microsoft Teams interacts with the Microsoft Graph API and a custom Copilot Studio agent to retrieve data from SharePoint and display it in Power BI.

Skills to revise

Microsoft Copilot Studio and Power Platform
Gain hands-on experience building conversational agents and automation workflows. Exercise: Build a simple intelligent agent in Copilot Studio that uses a Power Automate flow to fetch data from an external source and respond to user prompts.
Prompt Engineering and API Integration
Practice crafting effective prompts for LLMs and integrating them via REST APIs. Exercise: Develop a Python script that interacts with an Azure OpenAI endpoint, using a structured prompt to summarize a document and returning the result via a REST API call.
Microsoft Graph API
Learn to programmatically access data across the Microsoft 365 ecosystem. Exercise: Write a script to authenticate and retrieve specific user or file metadata from SharePoint or Outlook using the Microsoft Graph API, ensuring proper permission scopes are defined.

Your preparation checklist

Develop a Copilot Studio project walkthrough
Prepare a demonstration of a custom intelligent agent built in Microsoft Copilot Studio. Include how you integrated it with a business system using REST APIs or custom connectors and how you utilized Power Automate to trigger specific workflows based on user input.
Prepare evidence of LLM and prompt engineering expertise
Create a portfolio of prompt engineering examples that demonstrate your ability to optimize LLM outputs for specific business tasks. Be ready to explain your process for selecting appropriate models and how you handle prompt refinement to ensure accuracy and security compliance.
Map technical solutions to business value
Draft a summary of a past project where you translated a non-technical business requirement into a technical specification. Focus on how you identified the problem, selected the Microsoft 365 or Power Platform tool, and measured the resulting business impact or efficiency gain.
Formulate questions for the interviewers
Prepare thoughtful questions regarding the team's current approach to AI governance and security within the Microsoft ecosystem, how they balance client-specific customization with scalable architecture, and how the team stays updated on rapid advancements in generative AI.
Review AI and Analytics lifecycle methodologies
Be prepared to discuss your experience across the full AI lifecycle, specifically focusing on how you handle data preparation, model validation, and the ongoing monitoring of AI agents after deployment to ensure they continue to meet performance standards.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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