New opportunity

Product Manager – Voice AI & LLM Chatbots

Sense HQ · Bengaluru, India

About this role

Sense is seeking a Product Manager to lead the roadmap for Voice AI, LLM chatbots, and the Agent Builder platform. You will manage the product lifecycle from strategy and vendor selection to delivery, quality assurance, and customer outcomes. The role involves working within an EPD (Engineering, Product, Design) triad to build conversational AI products that handle real-time, non-deterministic interactions at scale. Responsibilities include defining agent behaviors, prompt architecture, and guardrails, as well as managing the voice stack (telephony, STT, TTS, LLMs) and ATS integrations. You will also establish quality evaluation frameworks, including A/B testing and observability, and ensure compliance with consent and data regulations. Required qualifications include 4–7 years of product management experience, a BE/B.Tech or MBA degree, and a proven track record of shipping LLM-powered, voice, or conversational products. You must possess a practical understanding of prompt design, failure modes in LLM agents, and evaluation approaches for non-deterministic systems. Hands-on experience with LLM tools (e.g., Claude, Gemini, ChatGPT) and strong analytical skills, including SQL and API integration knowledge, are essential. Preferred qualifications include experience with real-time voice systems, enterprise multi-tenant platforms, and exposure to HR tech or high-volume candidate engagement. You will be responsible for key metrics such as conversation outcomes, business impact, quality scores, reliability, unit economics, and user trust.

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

Skills for this role

Product ManagementVoice AILLMChatbotsAgent BuilderPrompt DesignGroundingGuardrailsAI EvaluationClaudeGeminiChatGPTLLM APIsSQLAPI IntegrationSystem DesignTelephonySpeech-to-TextText-to-SpeechHR Tech

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

Defining AI Quality and Evaluation Frameworks
The role requires establishing robust methods for measuring non-deterministic AI performance, including call sampling, A/B testing, and observability. You must prioritize building systems that allow the team to diagnose why specific conversations succeeded or failed to ensure consistent candidate outcomes.
Optimizing Unit Economics and Vendor Strategy
You are responsible for managing the voice stack, which involves making build-versus-buy decisions for telephony, STT, TTS, and LLM components. A key priority is balancing high-quality, low-latency interactions with cost-per-call efficiency to maintain healthy gross margins.
Managing Agent Behavior and Prompt Architecture
You must define the logic for the Agent Builder platform, including prompt design, guardrails, and ATS integration mapping. This ensures that conversational agents effectively screen candidates and write back accurate data to the ATS, directly impacting recruiter productivity.

Likely challenges to think through

Managing Non-Deterministic System Reliability
Because LLM-powered voice agents are inherently non-deterministic, you will face difficulty in guaranteeing consistent performance at scale. Prepare by demonstrating how you have previously managed 'failure modes' in AI agents and how you design guardrails to maintain user trust and compliance.
Balancing Real-Time Latency and Quality
Real-time voice interactions require extremely low latency, which often conflicts with the processing time needed for complex LLM reasoning. Prepare to discuss technical trade-offs in voice stack architecture, such as choosing between model speed and response accuracy.
Navigating Compliance in High-Volume Recruiting
Operating in HR tech involves strict consent and data regulation requirements for outbound calling. You will need to balance aggressive automation goals with the legal necessity of opt-outs and disclosures, requiring close collaboration with Legal and Customer Success teams.

Practice your answers

Technical practice

Managing Non-Deterministic AI Outputs

What to coverExplain how you define acceptance criteria for non-deterministic systems like LLM chatbots. Focus on how you move beyond binary pass/fail tests to probabilistic evaluation frameworks and how you handle edge cases where the model might hallucinate or deviate from the script.

Prompt Architecture and Guardrails

What to coverDescribe your approach to designing prompt architecture for conversational agents. Discuss how you implement guardrails to ensure the agent remains within scope, adheres to compliance requirements, and maintains a consistent brand voice during real-time interactions.

Voice Stack Optimization

What to coverHow do you balance the trade-offs between latency, voice quality, and cost per call when selecting telephony, STT, and TTS vendors? Suggestion: Prepare to discuss how you would model these unit economics to ensure profitability while maintaining a high-quality candidate experience.

Evaluation Frameworks for Conversational AI

What to coverDetail your methodology for building an evaluation framework for AI agents. Include how you use call sampling, A/B testing, and observability tools to diagnose why a conversation failed or why a specific module experienced high drop-off rates.

ATS Integration and Data Integrity

What to coverExplain the challenges of mapping conversational data to ATS fields and ensuring successful note writeback. Discuss how you handle data validation and error handling to ensure that automated conversations result in usable, high-quality records for recruiters.

Improving Unit Economics in LLM Products

What to coverDescribe strategies for optimizing the cost per conversation in an LLM-powered product. Discuss techniques such as prompt efficiency, model selection based on task complexity, and caching strategies to reduce API costs without sacrificing performance.

Handling Compliance in Automated Outreach

What to coverHow do you integrate consent, opt-out, and disclosure requirements into the design of an automated voice or SMS agent? Suggestion: Focus on how you ensure compliance is maintained throughout the conversation flow without creating unnecessary friction for the candidate.

System Design for Multi-Tenant Platforms

What to coverDiscuss the architectural considerations for building an enterprise multi-tenant platform that supports custom instructions and workflows for different customers. Explain how you ensure isolation and scalability while allowing for individual client configuration.

Behavioural practice

Navigating Ambiguity in Product Strategy

What to coverDescribe a time when you had to define a product roadmap for a highly ambiguous problem space. Use the STAR method to explain how you gathered requirements, aligned stakeholders, and translated business goals into actionable user stories.

Managing EPD Triad Collaboration

What to coverProvide an example of a conflict or disagreement within an EPD (Engineering, Product, Design) triad. Explain how you facilitated the discussion, balanced technical constraints with user experience, and reached a consensus that supported the product's success.

Turning Customer Feedback into Product Fixes

What to coverShare an experience where you identified a pattern in customer support escalations or feedback. Detail the steps you took to validate the issue, prioritize the fix, and measure the impact of the resulting product improvement.

Influencing Stakeholders through Data

What to coverGive an example of how you used data to influence a strategic decision or vendor selection process. Explain the metrics you tracked, how you presented the findings to executives, and how your recommendation ultimately impacted the business.

Sharpen your knowledge

Topics to prepare

Evaluation Frameworks for Non-Deterministic Systems
The role requires defining quality metrics for AI agents where outputs are not fixed. Exercise: Design a testing framework for a voice bot that includes call sampling, A/B testing, and latency measurement. This is critical for ensuring reliability and user trust in high-volume HR environments.
Unit Economics of Conversational AI
You must manage cost-per-call and prompt efficiency. Exercise: Create a model to compare the cost trade-offs between different LLM providers and speech-to-text vendors for a high-volume outbound calling scenario. This is essential for maintaining gross margins on the platform.
Prompt Architecture and Guardrails
The role involves defining how agents behave and stay within compliance. Exercise: Draft a prompt architecture for a candidate screening bot that includes specific guardrails to prevent hallucination and ensure adherence to data consent regulations.

Skills to revise

SQL for Product Analytics
You will need to analyze conversation outcomes and system performance. Exercise: Write a SQL query to calculate the 'human handoff rate' and 'drop-off by module' from a hypothetical dataset of chatbot interactions. This is necessary to identify product fixes and improve recruiter productivity.
API Integration and ATS Mapping
The role requires ensuring conversations become usable records in an ATS. Exercise: Map a set of candidate data points (e.g., availability, salary expectations) from a chat transcript to standard ATS fields, documenting the logic for handling missing or malformed data.
Hands-on LLM Prototyping
You must demonstrate practical experience with LLM tools. Exercise: Build a simple prompt-based workflow using an LLM API (e.g., Claude or ChatGPT) that classifies candidate responses into 'qualified' or 'unqualified' categories based on predefined criteria.

Your preparation checklist

Prepare a case study on LLM-powered product delivery
Draft a structured walkthrough of a conversational AI product you have shipped. Focus on how you managed non-deterministic outputs, defined prompt architecture, and implemented guardrails to ensure reliability and user trust.
Develop an AI evaluation framework proposal
Create a brief document outlining how you would measure the quality of a voice or chatbot agent. Include specific metrics for latency, completion rates, and hallucination detection, and explain how you would use A/B testing or simulation to validate performance.
Review technical fundamentals for voice and integration
Refresh your knowledge on the voice stack (STT, TTS, telephony) and API integration patterns. Be prepared to discuss how you would handle ATS data mapping and writebacks to ensure seamless workflow automation.
Model unit economics for AI products
Prepare to discuss how you balance product quality with cost. Create a hypothetical scenario where you must choose between different LLM or speech-to-text vendors based on latency, accuracy, and cost-per-call trade-offs.
Formulate strategic questions for the EPD team
Prepare thoughtful questions regarding Sense's current approach to agent observability, how they handle compliance and consent at scale, and how the EPD triad collaborates to prioritize technical debt versus new feature development.

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