Kapture CX is a leading SaaS platform that helps enterprises automate and elevate customer experience through intelligent, AI-powered solutions. We partner with enterprises across industries to bring scalable automation and insight-driven efficiencies to their CX operations. Over a thousand clients across 18 countries have used Kapture’s products to enhance their customer experience, including Unilever, Reliance, Coca-Cola, Bigbasket, Meesho, Airtel Payments Bank and Cathay Pacific.
Kapture CX is headquartered in Bangalore, with offices across India and globally. We have offices in Mumbai and Delhi/NCR in India, in addition to offices in the USA, UAE, Singapore, Philippines and Indonesia.
Location: Bengaluru (5 days in-office)
This is a leadership role responsible for scaling our Voice AI platform from 1 10 — improving reliability, performance, and conversational quality across deployments.
This is not a 0 1 role. The platform, use cases, and deployments already exist. The focus is on:
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making systems more robust and predictable
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improving real-world conversational quality
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scaling best practices across use cases
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building and leading a high-performing Applied AI team
A key part of this role is to systematically experiment with evolving AI capabilities and translate those learnings into production-ready improvements.
You will own this function end-to-end, combining hands-on depth with team leadership.
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Own performance, reliability, and conversational quality of voicebots across deployments
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Define prompting strategies, guardrails, fallback logic, and multi-agent configurations
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Drive continuous improvement using real-world data, structured experimentation, and rapid iteration
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Define and drive a structured experimentation framework across LLMs, STT/TTS systems, and orchestration approaches
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Benchmark models and configurations across latency, accuracy, cost, and conversational quality
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Run controlled experiments (A/B, shadow testing) using production-like scenarios
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Identify what works in real-world conditions and standardize those learnings
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Continuously evaluate new models, APIs, and techniques as the ecosystem evolves
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Drive improvements in how voicebots interact to feel natural, intuitive, and context-aware
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Improve turn-taking, interruptions, and response timing to reduce friction
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Enhance handling of real-world variability (accents, multilingual inputs, noisy ASR)
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Set quality benchmarks for tone, clarity, and contextual relevance
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Use production data to identify breakdowns and systematically improve conversations
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Oversee root cause analysis across prompts, models, integrations, and platform behavior
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Establish frameworks for diagnosing and resolving failures quickly and systematically
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Define best practices, playbooks, and reusable frameworks across deployments
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Drive consistency, predictability, and efficiency across all voice AI implementations
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Act as the bridge between real-world deployments and product evolution
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Translate field learnings into clear product requirements and roadmap inputs
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Partner closely with Product, ML, and Engineering to shape platform direction
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Build, mentor, and lead a team of Applied AI / Voice specialists
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Define operating models, quality standards, and execution frameworks
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Ensure strong ownership, problem-solving, and consistent output across the team
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Voice AI systems are reliable, scalable, and consistent across deployments
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Measurable improvement in conversational quality (task completion, user satisfaction, reduced drop-offs)
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Faster and more structured resolution of complex issues
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Clear, data-backed decisions on model and configuration choices
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Strong influence on product and platform evolution
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A high-performing team that owns Applied AI excellence end-to-end
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8+ years of overall experience with 2+ years in AI products, voicebots, conversational AI, or applied ML systems
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Strong hands-on expertise in prompting, experimentation, and system behavior
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Deep understanding of voice AI systems (ASR, TTS, latency, turn-taking, fallbacks)
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Experience evaluating models and making pragmatic trade-offs (latency, cost, quality)
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Proven experience leading or mentoring teams in technical/product environments
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Strong ownership mindset with ability to scale systems, processes, and people
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Have worked on voicebot platforms or real-time AI systems in production
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Have experience with multi-agent architectures or LLM orchestration
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Have prior experience in implementation/delivery and understand real-world constraints
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Own and lead a critical Applied AI function in a fast-growing AI company
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Work on real-world AI systems at scale, not just prototypes
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Influence both product direction and system performance
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Build and shape a high-impact team
