SquadStack is seeking a Product Manager - Voice AI to lead the development and strategy of our voice-based artificial intelligence products. This role focuses on building and scaling innovative voice solutions that enhance our platform's capabilities. Responsibilities include defining the product roadmap, collaborating with engineering and data science teams to implement voice AI features, and ensuring the product meets user needs and business objectives. Candidates should have a strong background in product management, specifically within the AI or voice technology domain. The role requires 3-5 years of experience. The position is based in Noida, India.
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Skills for this role
Product ManagementVoice AIConversational AI
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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
Voice AI Product Strategy
The primary focus is leading the development and strategic direction of voice-based AI products. You must demonstrate the ability to translate high-level business objectives into a coherent product roadmap that leverages voice technology to solve specific user needs.
Cross-functional Collaboration
Success in this role depends on effectively partnering with engineering and data science teams. You will need to bridge the gap between technical AI capabilities and practical product features, ensuring that development cycles remain aligned with business goals.
Scaling Voice Solutions
Beyond initial development, the role requires scaling innovative voice features within the existing platform. You must prioritize features that enhance platform capabilities while maintaining performance and user experience standards.
Likely challenges to think through
Balancing Technical Complexity with User Needs
Voice AI projects often face the challenge of over-engineering technical features that may not solve real user problems. Prepare to discuss how you validate AI-driven features with users and how you decide when to prioritize simplicity over technical sophistication.
Managing AI Development Uncertainty
AI development is inherently iterative and unpredictable compared to traditional software. You will likely face challenges in setting accurate timelines and managing stakeholder expectations when model performance or data quality issues arise.
03
Practice your answers
Technical practice
Voice AI Product Lifecycle Management
What to coverExplain your approach to managing the end-to-end lifecycle of a voice-based AI product. Focus on how you balance technical feasibility with user-centric design and business objectives.
Defining Voice AI Metrics
What to coverWhat key performance indicators (KPIs) would you prioritize to measure the success of a conversational AI product? Discuss how you differentiate between technical accuracy metrics like Word Error Rate (WER) and business-focused metrics like task completion rate.
Collaborating with Engineering and Data Science
What to coverDescribe your process for translating high-level product requirements into actionable technical specifications for AI/ML teams. How do you handle trade-offs between model performance and latency in voice applications?
Voice User Interface (VUI) Design Principles
What to coverWhat are the core principles you follow when designing for voice-first interactions? Address how you manage user expectations, handle ambiguity, and design for error recovery in conversational flows.
Scaling Voice Solutions
What to coverWhen scaling a voice AI product, what technical challenges do you anticipate regarding infrastructure and data processing? Suggest how you would prioritize feature development to ensure the platform remains robust as user volume grows.
Data Strategy for Voice AI
What to coverHow do you approach the collection, labeling, and management of voice data to improve model performance? Discuss the importance of data diversity and ethical considerations in training conversational AI.
Behavioural practice
Prioritizing Conflicting Product Requirements
What to coverDescribe a situation where you had to choose between two competing product features or technical improvements. Explain your decision-making process and how you communicated the trade-offs to stakeholders.
Managing Cross-Functional Alignment
What to coverProvide an example of a time when you had to align engineering, data science, and business teams on a complex AI project. How did you resolve disagreements regarding the product roadmap?
Handling Product Failure or Underperformance
What to coverTell me about a time when a voice AI feature did not perform as expected after launch. What steps did you take to diagnose the issue, and how did you iterate based on the feedback?
Adapting to Rapid AI Advancements
What to coverThe AI field evolves quickly; describe a time you had to pivot your product strategy due to a new technological development. How did you ensure the team remained focused and productive during the transition?
02
Sharpen your knowledge
Topics to prepare
Voice AI Product Strategy
Review frameworks for defining product roadmaps specifically for voice-based interfaces. Exercise: Draft a hypothetical 6-month roadmap for a voice-AI feature, identifying key milestones for user testing and performance metrics. This is relevant as the role requires defining the strategy and roadmap for voice-based AI products.
Cross-functional Collaboration in AI Development
Study best practices for bridging the gap between product requirements and technical implementation with engineering and data science teams. Exercise: Create a communication plan for translating user needs into technical requirements for a voice-AI model. This is essential for the role's requirement to collaborate with engineering and data science teams.
Skills to revise
Conversational AI Design
Practice designing user flows for voice interactions, focusing on intent recognition and error handling. Exercise: Map out a user journey for a voice-based task, documenting how the system should handle ambiguous inputs. This directly supports the requirement to build innovative voice solutions that meet user needs.
AI Product Lifecycle Management
Develop a process for monitoring and scaling AI features post-launch. Exercise: Define three key performance indicators (KPIs) to measure the success and accuracy of a voice-AI product. This is critical for the role's focus on scaling products and ensuring they meet business objectives.
04
Your preparation checklist
Analyze Voice AI Product Lifecycle
Prepare a case study of a voice-based product you have managed or analyzed. Focus on the specific challenges of conversational AI, such as latency, intent recognition accuracy, and user experience design in voice interfaces.
Map Voice AI Metrics to Business Objectives
Develop a framework for measuring the success of a voice AI feature. Identify key performance indicators (KPIs) that connect technical performance, such as speech-to-text accuracy, to business outcomes like user retention or operational efficiency.
Prepare Technical Collaboration Examples
Draft specific examples of how you have worked with engineering and data science teams to translate product requirements into technical specifications for AI models. Highlight your approach to managing trade-offs between model performance and product delivery timelines.
Formulate Strategic Questions for the Interview
Prepare thoughtful questions regarding SquadStack's current voice AI roadmap, the primary user pain points they are solving with voice technology, and how the product team balances innovation with platform scalability.
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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