Senior Technical Product Manager – Applied AI

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