Product Manager

Overview

Own the quality of Bolna's live voice agent conversations and the observability tools that show why a call went wrong, for enterprise customers at scale.

What you'll do

• Own the quality of the live conversation: background noise and noise cancellation, turn-taking and interruptions, latency, and language switching.
• Drive agent memory: context that carries across calls, between steps of a multi-step agent, and through agent-to-agent transfers.
• Work with ML engineers on model choices and the models we build, define what "better" means, build the eval set, and decide when an in-house model is ready to replace a vendor.
• Turn fuzzy complaints from FDEs and customers into metrics with clear thresholds.
• Make it fast to find out why a call went wrong: which turn, which component (telephony, speech recognition, LLM, voice, tools), and what to change.
• Shape the dashboards, call-level debugging views and automated evals, including LLM-as-judge, that FDEs and customers use every day.
• Define the handful of quality and reliability metrics the company watches, and own their accuracy.
• Spot recurring failure patterns across thousands of calls before customers report them.

What you'll need

• 4+ years in product management, or in an engineering, consulting, analytics or founding role where you owned what got built and why.
• You've shipped products that moved a metric you can name.
• You communicate clearly in writing and in person.
• You take responsibility for outcomes, not tickets, and follow up until the fix works in production.
• You're comfortable handling many small problems in a week and one hard problem over a quarter.
• You can work independently, and you also work well alongside other PMs and engineering leads.

Nice to have

• Problem solving: you break messy, ambiguous problems into clear parts, find the root cause, and pick the simplest fix that works.
• Data sense: you go to the data before forming a view, write your own SQL, can tell a real pattern from noise, and notice when a number looks wrong and work out why.
• Technical understanding: you know how systems fit together and can reason about LLMs, APIs, latency and trade-offs. You read logs and API docs and hold your own in design discussions with engineers. You don't need to write production code.
• Experience with voice AI, conversational AI, LLM products, or developer and infrastructure platforms.
• Hands-on with LLMs: prompting, evals, LLM-as-judge, or prototypes you vibe-coded yourself.
• Exposure to speech technology: speech recognition, text-to-speech, noise suppression, or telephony.
• You've worked closely with enterprise customers or with forward-deployed or solutions teams.

Details

• Location: Bengaluru office, in-person team collaboration.