Product Manager

Sense; Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Benefits offered by this job

Mentorship and development support

Job summary

Sense is hiring a Product Manager to own a major part of our Voice AI, LLM chatbot, and Agent Builder roadmap, from strategy and vendor decisions to delivery, quality, and customer outcomes.

You will work on Sense's conversational AI products and set direction for your area with Product Leadership. The role involves shaping strategy for voice agents, LLM chatbots, and the Agent Builder platform, collaborating with AI/ML, Engineering, and Design teams in Bengaluru.

Qualifications

• BE/B.Tech or MBA, with 4–7 years of product management experience. Excellent academics.
• Shipped at least one LLM-powered, voice, or conversational product used by real customers.
• Practical understanding of prompt design, grounding, guardrails, and failure modes of LLM agents.
• Experience defining quality and evaluation approaches for AI or other non-deterministic systems.
• Hands-on experience working with LLM tools such as Claude, Gemini, and ChatGPT, for example through a personal, academic, or work project using LLM APIs, prompts, chatbots, or voice agents.
• Strong analytical skills, including SQL and working with API and integration behavior.
• Good system design skills and knowledge of the software development life cycle.
• Excellent written and oral communication, including executive presentations.
• Ability to prioritize and balance multiple tasks, work well in a team, and apply feedback constructively.

Responsibilities

• Own the roadmap for one or more areas: voice agent quality and evals, Agent Builder platform capabilities, voice cost and architecture, or ATS/workflow integrations.
• Turn customer and business problems into PRDs, user stories, and acceptance criteria for non-deterministic systems.
• Lead prioritization, sprint planning, and release coordination with Engineering, AI/ML, and Design.
• Define call outcomes and classifiers, such as consent revocation and handoffs.
• Define ATS integration behavior, including field mapping and writeback.
• Collaborate with Legal and CS on consent, opt-out, and disclosure requirements.
• Triage escalations, identify patterns in support tickets, and drive product fixes.

Skills

Product management

AI/LLM product

SQL

System design

Executive communication

Education

BE/B.Tech or MBA

Tools

Claude

Gemini

ChatGPT

Job description

Product Manager – Voice AI & LLM Chatbots

Sense is hiring a Product Manager to own a major part of our Voice AI, LLM chatbot, and Agent Builder roadmap, from strategy and vendor decisions to delivery, quality, and customer outcomes.

About The Role

You will work on Sense's conversational AI products and set direction for your area with Product Leadership.

• Voice AI and AI Receptionist: outbound and inbound agents that screen candidates, collect data, answer questions, and hand off to recruiters.
• LLM chatbots: web and SMS agents built on the same platform.
• Agent Builder: a modular platform (Greeting, Data Collection, Screening, FAQ, Closing) with custom instructions, prompt architecture, ATS variables, testing tools, and publishing into Sense Workflows.
• Voice stack: telephony, speech-to-text, LLMs, and text-to-speech from multiple vendors, where latency, voice quality, and cost per call are product decisions.
• Integrations: ATS field mapping and note writeback, which decide whether a good conversation becomes a usable record.

The work is hard because outputs are non-deterministic, calls happen in real time, and staffing customers run high volumes under consent and compliance rules. You will work in the EPD (Engineering, Product, Design) triad with AI/ML, Customer Success, Implementation, and GTM teams.

What You'll Do

Strategy

• Own the roadmap and outcomes for one or more areas: voice agent quality and evals, Agent Builder platform capabilities, voice cost and architecture, or ATS and workflow integrations.
• Find where conversational AI can improve recruiter productivity, candidate experience, and outcomes such as screening completion and submission-to-placement conversion.
• Track advances in LLMs, speech models, and voice orchestration, and judge which are worth adopting.

Execution

• Turn ambiguous customer and business problems into PRDs, user stories, and acceptance criteria that work for non-deterministic systems.
• Lead prioritization, sprint planning, and release coordination with Engineering, AI/ML, and Design.
• Work with QA on test coverage and run User Acceptance Testing before release.

Platform and vendors

• Define agent behaviors, modules, prompt architecture, and guardrails with AI/ML and Engineering.
• Make build-versus-buy and vendor decisions for speech, voice, and LLM components, with cost and quality trade-offs modeled.
• Improve unit economics, including prompt efficiency and cost per call.
• Define call outcomes and classifiers, such as consent revoked, human contact requested, and logical completion.
• Own ATS integration behavior, including field mapping and writeback.

Quality, evals, and compliance

• Build the approach to agent quality: call sampling, pre-release simulation, A/B testing, and voice-quality metrics.
• Improve observability so teams can see why a conversation went the way it did.
• Work with Legal and CS on consent, opt-out, and disclosure requirements.

Customers and stakeholders

• Run discovery sessions, demos, and roadmap conversations with customers, recruiters, and Implementation teams.
• Triage escalations, spot patterns across support tickets, and turn them into product fixes.
• Support pilots and account health reviews, and present to executives.

Metrics you'll own

Area

Example metrics

Conversation outcomes

• Connect rate, call and chat completion rate, drop-off by module, human handoff rate

Business impact

• Screening completion, submission-to-placement conversion, recruiter time saved

Quality

• Evaluation scores, voice-quality measures, interruption and latency quality, issue volume by theme

Reliability

• ATS writeback success rate, platform and vendor uptime, escalation volume

Economics

• Cost per call, gross margin per bot, LLM and speech cost per conversation

Trust

• Opt-out and complaint rates, compliance check pass rate

Adoption

• Active bots and accounts, time to launch a new bot, customer satisfaction

Qualifications

Must have

• BE/B.Tech or MBA, with 4–7 years of product management experience. Excellent academics.
• Shipped at least one LLM-powered, voice, or conversational product used by real customers.
• Practical understanding of prompt design, grounding, guardrails, and failure modes of LLM agents.
• Experience defining quality and evaluation approaches for AI or other non-deterministic systems.
• Hands-on experience working with LLM tools such as Claude, Gemini, and ChatGPT, for example through a personal, academic, or work project using LLM APIs, prompts, chatbots, or voice agents.
• Strong analytical skills, including SQL and working with API and integration behavior.
• Good system design skills and knowledge of the software development life cycle.
• Excellent written and oral communication, including executive presentations.
• Ability to prioritize and balance multiple tasks, work well in a team, and apply feedback constructively.

Nice to have

• Experience with real-time voice systems such as telephony, speech-to-text, or text-to-speech.
• Experience with enterprise, multi-tenant configuration platforms.
• Exposure to HR tech, staffing, or high-volume candidate engagement.
• Familiarity with consent and compliance requirements for outbound calling and messaging.

Why join

You will work on products that talk to candidates at scale, with real customers and measurable outcomes. The team is small enough that you will influence architecture, vendor, and roadmap decisions early. Sense offers active mentorship, learning and development support, and a path to larger product leadership roles.