Who are we?
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. W
e 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)
What is this role all about?
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:
making systems more robust and predictable
improving real-world conversational quality
scaling best practices across use cases
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.
What will you do?
Applied AI Ownership
Own performance, reliability, and conversational quality of voicebots across deployments
Define prompting strategies, guardrails, fallback logic, and multi-agent configurations
Drive continuous improvement using real-world data, structured experimentation, and rapid iteration
Applied Experimentation & Model Evaluation
Define and drive a structured experimentation framework across LLMs, STT/TTS systems, and orchestration approaches
Benchmark models and configurations across latency, accuracy, cost, and conversational quality
Run controlled experiments (A/B, shadow testing) using production-like scenarios
Identify what works in real-world conditions and standardize those learnings
Continuously evaluate new models, APIs, and techniques as the ecosystem evolves
Conversational Quality & Experience
Drive improvements in how voicebots interact to feel natural, intuitive, and context-aware
Improve turn-taking, interruptions, and response timing to reduce friction
Enhance handling of real-world variability (accents, multilingual inputs, noisy ASR)
Set quality benchmarks for tone, clarity, and contextual relevance
Use production data to identify breakdowns and systematically improve conversations
System Thinking & Debugging
Oversee root cause analysis across prompts, models, integrations, and platform behavior
Establish frameworks for diagnosing and resolving failures quickly and systematically
Scale & Standardization (1 → 10)
Define best practices, playbooks, and reusable frameworks across deployments
Drive consistency, predictability, and efficiency across all voice AI implementations
Product Influence
Act as the bridge between real-world deployments and product evolution
Translate field learnings into clear product requirements and roadmap inputs
Partner closely with Product, ML, and Engineering to shape platform direction
Team Leadership
Build, mentor, and lead a team of Applied AI / Voice specialists
Define operating models, quality standards, and execution frameworks
Ensure strong ownership, problem-solving, and consistent output across the team
What does success look like?
Voice AI systems are reliable, scalable, and consistent across deployments
Measurable improvement in conversational quality (task completion, user satisfaction, reduced drop-offs)
Faster and more structured resolution of complex issues
Clear, data-backed decisions on model and configuration choices
Strong influence on product and platform evolution
A high-performing team that owns Applied AI excellence end-to-end
What would make you a good fit?
8+ years of overall experience with 2+ years in AI products, voicebots, conversational AI, or applied ML systems
Strong hands-on expertise in prompting, experimentation, and system behavior
Deep understanding of voice AI systems (ASR, TTS, latency, turn-taking, fallbacks)
Experience evaluating models and making pragmatic trade-offs (latency, cost, quality)
Proven experience leading or mentoring teams in technical/product environments
Strong ownership mindset with ability to scale systems, processes, and people
You will have an advantage if you:
Have worked on voicebot platforms or real-time AI systems in production
Have experience with multi-agent architectures or LLM orchestration
Have prior experience in implementation/delivery and understand real-world constraints
Why should you be interested?
Own and lead a critical Applied AI function in a fast-growing AI company
Work on real-world AI systems at scale, not just prototypes
Influence both product direction and system performance
Build and shape a high-impact team
