AI Conversational Engineer

Qapitol QAS Private Limited; Bengaluru; On-site; INR 1,100,000 - 1,900,000; Full time

Job summary

Qapitol QAS Private Limited in Bengaluru is seeking an experienced QA professional focused on conversational AI, NLP, and ML product testing. You will validate multi-turn dialogues, ensure reliability, and improve test coverage across AI systems.

You will define KPIs, monitor intent accuracy and response latency, and build tooling and CI/CD test strategies to catch failures before production. You will work hands-on with Python/JavaScript and API testing in a fast-changing AI environment.

Qualifications

• 3+ years of QA experience, ideally on AI, ML or NLP products.
• Testing conversational systems for state, context handling, non-determinism and long-tail inputs.
• Proficient in Python, JavaScript, and API testing.
• Familiar with CI/CD pipelines and automation.
• AI-native with fluency in AI tooling and enthusiasm for AI applications.

Responsibilities

• Own quality across conversational AI systems, dialogue models and end-to-end flows.
• Define KPIs such as intent accuracy, response latency and conversation success rate.
• Build tooling and test strategies to catch failures before production as models evolve.

Skills

QA experience

AI/NLP testing

Python

JavaScript

API testing

CI/CD

AI tooling

Context handling

Tools

N/A

Job description

• Location on website: Bangalore
• Suggested Keka location: Bengaluru
• Job type: Full Time
• Experience: 3+ years
• Website link: https://qapitol.ai/careers/ai-conversational-engineer

You own quality across conversational AI systems, dialogue models, LLM-backed integrations and end-to-end flows. Your work is what makes intent recognition trustworthy and multi-turn behaviour predictable in production.

Conversational AI fails in ways that are hard to see until a user hits them: intent mismatches, context collapse mid-dialogue, hallucinated responses that sound plausible. This role exists to catch those failures before they reach production, and to build the tooling and test strategy that keeps catching them as models evolve.

You will work across NLP pipelines, LLM integrations and full conversational workflows, validating not just whether a response comes back but whether it is accurate, contextually appropriate and usable. You will define the KPIs, intent accuracy, response latency, conversation success rate, that give the team an honest signal on quality.

Who thrives here

You have spent at least three years in QA, ideally on AI, ML or NLP products, and you understand what makes conversational systems genuinely difficult to test: state, context, non-determinism and the long tail of unexpected inputs. You are comfortable in code, Python, JavaScript or similar, and you know your way around API testing and CI/CD pipelines. You are also expected to be AI-native: fluent in AI tooling, and curious about the applications reshaping your domain.