Accenture is seeking an AI Product Management Practitioner to lead the development of AI-driven products from its Pune office. The role defines product vision and strategy, works with data scientists, engineers and other stakeholders to deliver AI solutions, and addresses ethical considerations throughout development.
Responsibilities
Facilitate cross-functional collaboration and align teams on product goals and objectives.
Analyze market trends and user feedback to guide product development and enhancements.
Monitor AI model performance and user feedback, adjusting models as needed to meet evolving user and market needs.
Work independently, develop subject-matter expertise, contribute to team discussions and help solve work-related problems.
Requirements
Proficiency in AI Agents and Workflow Integration, with a stated minimum of 3 years of experience in this area. The posting also lists an experience range of 2–5 years and states that a minimum of 3 years is required.
Strong understanding of AI model performance metrics and evaluation techniques; experience in product lifecycle management and agile methodologies; and the ability to explain complex AI concepts to non-technical stakeholders.
15 years of full-time education.
Preferred skills: Agile Program Management, Generative AI, Retrieval-Augmented Generation, Large Language Models, Machine Learning Operations, Agile Project Management and Change Management.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
Skills for this role
AI AgentsWorkflow IntegrationAgile Program ManagementGenerative AIRetrieval-Augmented GenerationLarge Language Models (LLMs)Machine Learning OperationsAgile Project ManagementChange ManagementAI model evaluationProduct lifecycle managementAgile methodologiesStakeholder communicationCross-functional collaboration
Your skill match
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YOUR NEXT STEP
Get interview-ready for this role
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
AI Agent and Workflow Integration
As the core technical requirement, you must demonstrate deep expertise in designing and managing AI agents within complex workflows. Prepare to discuss how you architect agentic systems, handle state management, and integrate these agents into existing business processes to deliver measurable value.
Cross-Functional Stakeholder Alignment
You are responsible for bridging the gap between technical teams (data scientists, engineers) and non-technical stakeholders. Focus on your ability to translate complex AI model performance metrics into business-relevant insights and ensure all parties are aligned on product vision and ethical standards.
Iterative Model Performance Management
The role requires continuous monitoring and adjustment of AI models based on real-world performance and user feedback. Prepare to explain your methodology for evaluating model efficacy, identifying drift or performance degradation, and managing the product lifecycle to ensure ongoing relevance.
Likely challenges to think through
Balancing Technical Depth with Product Strategy
You must act as a subject matter expert while maintaining a high-level product vision. The challenge is to avoid getting lost in technical implementation details while still providing enough guidance to solve complex problems; prepare by practicing how to pivot between high-level strategy and technical troubleshooting.
Managing Ethical Considerations in AI Development
Addressing ethics is a stated core responsibility. You may face pressure to prioritize speed-to-market over rigorous ethical vetting. Prepare to discuss your framework for identifying and mitigating bias, privacy risks, and safety concerns throughout the development lifecycle.
03
Practice your answers
Technical practice
Designing AI Agent Workflows
What to coverExplain your approach to architecting AI agent workflows for complex business processes. Focus on how you ensure reliability, handle error states, and manage hand-offs between different agents or human-in-the-loop systems.
AI Model Evaluation Metrics
What to coverDiscuss the specific metrics you use to evaluate the performance of generative AI models versus traditional machine learning models. Suggestion: Prepare to explain how you balance technical accuracy metrics with user-centric feedback loops.
Implementing Retrieval-Augmented Generation (RAG)
What to coverDescribe the challenges of implementing RAG in a production environment. Focus on data retrieval accuracy, context window management, and how you mitigate hallucinations in the generated output.
Machine Learning Operations (MLOps) Lifecycle
What to coverHow do you manage the lifecycle of an AI product from initial deployment to ongoing monitoring? Address how you handle model drift and the process for retraining or updating models based on real-world performance data.
Communicating AI Concepts to Non-Technical Stakeholders
What to coverProvide an example of how you explain complex AI concepts, such as model limitations or probabilistic outcomes, to business stakeholders. Suggestion: Focus on translating technical risks into business impact and actionable strategy.
Ethical Considerations in AI Development
What to coverHow do you integrate ethical considerations into the product development lifecycle? Discuss your approach to identifying potential biases in training data or model outputs and the mitigation strategies you employ.
Agile Methodologies in AI Product Management
What to coverExplain how you adapt traditional Agile project management for the iterative and often unpredictable nature of AI development. Focus on how you manage scope and expectations when model performance is uncertain.
Behavioural practice
Facilitating Cross-Functional Alignment
What to coverDescribe a situation where you had to align data scientists, engineers, and business stakeholders on a conflicting product goal. Focus on the specific actions you took to facilitate communication and the final outcome.
Managing Product Strategy Based on User Feedback
What to coverGive an example of a time you had to pivot or adjust an AI product's strategy based on user feedback or market trends. Explain how you analyzed the feedback and the impact of your subsequent adjustments.
Problem Solving as a Subject Matter Expert
What to coverDescribe a time you were required to solve a complex, work-related problem independently. Detail the steps you took to research the issue, consult necessary resources, and implement a solution.
Handling Change Management
What to coverDiscuss a project where you had to lead change management efforts to ensure the successful adoption of a new AI solution. Explain how you addressed resistance and ensured stakeholders were prepared for the transition.
02
Sharpen your knowledge
Topics to prepare
AI Model Evaluation and Performance Metrics
The role requires monitoring AI model performance and adjusting based on feedback. Exercise: Create a framework for evaluating an LLM-based product, defining specific KPIs for accuracy, latency, and user satisfaction, and map these to potential corrective actions.
AI Ethics and Responsible Development
The job description explicitly mentions addressing ethical considerations throughout the product lifecycle. Exercise: Draft a checklist for identifying potential bias or ethical risks in an AI agent workflow and propose mitigation strategies for each identified risk.
Product Lifecycle Management for AI
You must manage AI products from vision to post-launch monitoring. Exercise: Map out a typical AI product lifecycle, identifying key decision points where you would transition from development to deployment and how you would incorporate user feedback loops.
Skills to revise
AI Agents and Workflow Integration
This is the primary required skill. Exercise: Design a high-level architecture for an AI agent that automates a multi-step business process, detailing how the agent interacts with external tools and handles workflow exceptions.
Stakeholder Communication of Technical Concepts
You must explain complex AI concepts to non-technical stakeholders. Exercise: Prepare a 5-minute presentation explaining the benefits and limitations of Retrieval-Augmented Generation (RAG) to a non-technical business stakeholder.
Agile Methodologies for AI Projects
The role requires experience in agile project management. Exercise: Propose a sprint structure for an AI development project that balances the iterative nature of model training with the structured requirements of product delivery.
04
Your preparation checklist
Prepare an AI Agent Workflow Case Study
Develop a concise walkthrough of a past project where you integrated AI agents into a business workflow. Focus on the specific problem solved, the technical architecture, and how you measured success using AI performance metrics.
Draft a Non-Technical AI Concept Explanation
Prepare a clear, jargon-free explanation of a complex AI concept like Retrieval-Augmented Generation (RAG) or LLM fine-tuning. This demonstrates your ability to bridge the gap between technical data science teams and business stakeholders.
Map AI Lifecycle to Agile Methodologies
Create a visual or written summary of how you manage the AI product lifecycle within an Agile framework. Include how you handle model evaluation, feedback loops, and iterative improvements based on real-world performance data.
Formulate Strategic Questions for the Interviewer
Prepare thoughtful questions regarding Accenture's approach to AI ethics, how they prioritize AI product roadmaps, and how they currently manage the transition from experimental AI models to production-ready solutions.
Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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