Who We Are

About The Opportunity

What We Are Looking For

You have hands-on experience building and operating conversational AI products in production — not just shipping agents, but owning the quality systems, data pipelines, and operational platforms that keep them reliable at scale. Ideal candidates will have background in one or more of the following areas:

• Knowledge Base & Data Quality — knowledge base architecture, retrieval quality tuning, content governance, labeling pipelines, annotation guidelines, training data impact tracking, and dataset freshness management
• Agent Evaluation & Quality Assurance — evaluation harness design, test case schemas, automated scoring rubrics (correctness, groundedness, tool-use accuracy), LLM-as-judge evaluation, regression testing for non-deterministic systems, and feedback-driven improvement loops
• Chatbot Operations & Dialogue Design — SOP-to-agent-flow translation, edge case handling, escalation path design, log-based failure triage, and metrics ownership (resolution rate, fallback rate, per-intent accuracy, CSAT)
• Agent Runtime & Observability Platforms — agent runtime product requirements, tool permission models, task configuration interfaces, developer-facing observability dashboards, failure alerting logic, and debugging workflows
• Human-in-the-Loop Workflows — low-confidence case routing, reviewer task interface design, correction data capture, and feedback loop integration back into training or knowledge pipelines

Chatbot & Knowledge Base (Core)

• Built or rebuilt a knowledge base — defined structure, wrote/reviewed content, fixed retrieval quality, saw metrics improve
• Designed SOPs that became agent flows — mapped real business processes, handled edge cases, shipped as working dialogue flows
• Owned a labeling pipeline — wrote annotation guidelines, QA'd batches, tracked whether labeled data moved production metrics
• Moved a metric that mattered — resolution rate, fallback rate, CSAT — and can explain exactly what changed

Agent Harness & Platform Product (Strong Plus)

• Designed an agent evaluation harness: defined test case schemas, scoring rubrics, and spec'd automated evaluation pipelines with engineering
• Product-designed an internal agent platform: defined requirements for agent runtime — tool permission models, task configuration interfaces, developer-facing observability dashboards, and failure debugging workflows; owned the roadmap and shipped iteratively
• Closed the eval-to-improvement loop: used harness output to prioritize knowledge fixes, prompt revisions, or flow changes — not just reported scores but drove action from them
• Designed human-in-the-loop review workflows: low-confidence case routing, reviewer task interfaces, correction data capture and feedback loop back into training or knowledge pipelines

What You’ll Be Doing 

What We Look For In You

Nice-To-Haves 

Perks & Benefits 

• Competitive total compensation package
• L&D programs and education subsidy for employees' growth and development
• Various team building programs and company events
• Wellness and meal allowances
• Comprehensive healthcare schemes for employees and dependants
• More that we love to tell you along the process!

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