New opportunity

AI Product Management Practitioner

Accenture · Pune, India

About this role

The AI Product Management Practitioner leads the development of AI-driven products at Accenture’s Pune office. The role defines product vision and strategy, works with data scientists, engineers and other stakeholders to create AI solutions, addresses ethical considerations, and adjusts AI models in response to performance and user feedback.

Responsibilities

Lead cross-functional collaboration and align teams on product goals and objectives.
Participate actively in team discussions, solve work-related problems independently and develop subject-matter expertise.
Analyze market trends and user feedback to guide product development and enhancements.
Monitor AI model performance and communicate complex AI concepts to non-technical stakeholders.

Requirements

At least 7.5 years of experience overall, including at least 3 years in AI Agents & Workflow Integration. The listing header also states an experience range of 5–10 years.
Proficiency in AI Agents & Workflow Integration, a strong understanding of AI model performance metrics and evaluation techniques, and experience in product lifecycle management and agile methodologies.
15 years of full-time education.

Preferred skills include Agile Program Management, Generative AI, Large Language Models, Retrieval-Augmented Generation, Machine Learning Operations, Agile Project Management and Change Management. This is a full-time position based at the Pune office.

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

Skills for this role

AI Agents & Workflow IntegrationAgile Program ManagementGenerative AIRetrieval-Augmented GenerationLarge Language Models (LLMs)Machine Learning OperationsAgile Project ManagementChange ManagementAI model evaluationProduct lifecycle managementAgile methodologiesStakeholder communication

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

AI Agents and Workflow Integration Leadership
The role requires deep expertise in orchestrating AI agents and integrating them into complex workflows. You must demonstrate how you have architected or managed systems where multiple agents interact to achieve business outcomes, as this is the core technical requirement for the position.
Cross-Functional Stakeholder Alignment
You must bridge the gap between technical teams (data scientists and engineers) and non-technical stakeholders. Success depends on your ability to translate complex AI performance metrics and model behaviors into actionable business strategy and product vision.
Iterative AI Model Lifecycle Management
The role emphasizes continuous improvement through monitoring and feedback loops. You need to show proficiency in using performance metrics to refine models and adjust product strategy, ensuring the AI solution remains aligned with user needs and ethical standards.

Likely challenges to think through

Translating Technical Complexity for Business Impact
A significant challenge is communicating the nuances of AI model performance to non-technical stakeholders who may expect immediate, perfect results. Prepare by practicing how to explain the trade-offs between model accuracy, latency, and cost in a way that supports business decision-making.
Managing Ethical and Performance Constraints
Balancing rapid AI development with ethical considerations and rigorous evaluation is difficult. Prepare by developing a framework for how you identify and mitigate bias or performance degradation in real-time, ensuring that your product development process includes built-in safety and quality checkpoints.

Practice your answers

Technical practice

Designing AI Agents and Workflows

What to coverExplain your approach to architecting AI agents that automate complex business workflows. Focus on how you ensure reliability, handle state management, and integrate these agents into existing enterprise systems.

AI Model Performance Evaluation

What to coverDescribe the metrics you prioritize when evaluating the performance of an AI product. Discuss the trade-offs between technical accuracy metrics and business-centric KPIs like user satisfaction or operational efficiency.

Implementing Retrieval-Augmented Generation (RAG)

What to coverDiscuss the challenges of implementing RAG in a production environment. Address how you manage data retrieval latency, context window limitations, and the accuracy of the generated responses.

Managing the AI Product Lifecycle

What to coverHow does the lifecycle of an AI-driven product differ from traditional software? Focus on the iterative nature of model training, data drift monitoring, and the continuous feedback loops required for improvement.

Communicating AI Concepts to Stakeholders

What to coverProvide a strategy for explaining complex AI model behaviors or limitations to non-technical stakeholders. Suggestion: Focus on translating technical risks into business impact and managing expectations regarding model uncertainty.

Addressing Ethical Considerations in AI

What to coverHow do you identify and mitigate ethical risks such as bias or data privacy concerns during the product development process? Explain how you integrate these checks into your standard agile development cycle.

Applying MLOps Principles

What to coverDescribe how you ensure the scalability and maintainability of AI solutions. Discuss the importance of CI/CD pipelines for models and how you handle versioning for both data and model artifacts.

Leveraging LLMs for Product Features

What to coverWhen deciding to integrate Large Language Models into a product, what criteria do you use to evaluate cost, latency, and performance? Suggestion: Discuss the trade-offs between using proprietary APIs versus open-source models.

Behavioural practice

Leading Cross-Functional Alignment

What to coverDescribe a situation where you had to align data scientists, engineers, and business stakeholders on a conflicting product goal. Explain the steps you took to reach a consensus and the final outcome.

Problem Solving as an SME

What to coverGive an example of a time you identified a significant technical or process-related problem independently. Detail the actions you took to resolve it and how you shared your findings to improve team expertise.

Adapting to User Feedback

What to coverShare an experience where user feedback or performance data indicated that an AI model was not meeting expectations. Describe how you adjusted the product strategy or model parameters to address the issue.

Managing Change in AI Projects

What to coverDescribe a project where you had to manage significant organizational change due to the introduction of new AI technology. How did you handle resistance and ensure the team remained productive?

Sharpen your knowledge

Topics to prepare

AI Model Performance and Evaluation
Review standard metrics for evaluating AI models, such as precision, recall, F1-score, and latency, as well as domain-specific evaluation techniques for LLMs. Exercise: Create a mock evaluation framework for a hypothetical RAG-based customer support bot, defining which metrics you would track to measure accuracy and user satisfaction.
AI Ethics and Responsible AI
Understand the ethical considerations in AI product development, including bias mitigation, data privacy, and transparency. Exercise: Draft a checklist for identifying potential ethical risks in an AI agent workflow, focusing on how to address these risks during the product design phase.
Product Lifecycle Management in AI
Study the end-to-end lifecycle of AI products, from ideation and data collection to deployment and continuous monitoring. Exercise: Map out a product roadmap for an AI agent, identifying key milestones for model retraining and feedback integration based on real-world performance data.

Skills to revise

AI Agents & Workflow Integration
Focus on designing and orchestrating autonomous agents that perform multi-step tasks. Exercise: Design a workflow diagram for an AI agent that automates a common business process, such as document processing or lead qualification, ensuring clear hand-offs between steps.
Stakeholder Communication of AI Concepts
Practice translating technical AI concepts into business value for non-technical stakeholders. Exercise: Prepare a 3-minute pitch explaining the benefits and limitations of a Retrieval-Augmented Generation (RAG) system to a non-technical executive, focusing on ROI and risk management.
Agile Methodology for AI Projects
Apply agile frameworks to the iterative nature of AI development. Exercise: Propose a sprint structure for an AI project that accounts for the uncertainty of model performance, specifically how to manage 'spikes' for model experimentation within a standard two-week sprint.

Your preparation checklist

Prepare an AI Agent project case study
Draft a detailed walkthrough of a product you managed involving AI Agents and workflow integration. Focus on the specific problem solved, the architecture of the agentic workflow, and how you measured success using AI-specific performance metrics.
Develop a framework for non-technical stakeholder communication
Create a sample presentation or document that explains a complex AI concept, such as RAG or model drift, to a non-technical audience. Demonstrate how you translate technical model performance data into actionable business insights.
Map your experience to AI lifecycle management
Prepare a timeline of your past projects highlighting your role in the full product lifecycle, specifically emphasizing how you incorporated user feedback to adjust model performance and addressed ethical considerations during development.
Formulate strategic questions for the interview
Prepare questions regarding Accenture's approach to MLOps, how the team balances rapid Generative AI innovation with ethical AI standards, and how cross-functional teams in the Pune office are structured to support AI product development.

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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