We are looking for an autonomous software engineer with a strong data and analytics focus to build the data foundations behind AI-powered CCaaS/UCaaS product features.

This role is responsible for designing and implementing the data pipelines, analytical services, and AI-enabling data layers that power transcription analytics, LLM-driven insights, QA automation, and reporting across the platform.

While this is still a full-stack role, the centre of gravity is data engineering, analytics, and AI data flows, with enough backend and frontend awareness to deliver end-to-end product capabilities.

What You ll Do

AI & Data Engineering

Design and build data pipelines supporting: Implement analytics-ready data models for AI features such as agent performance, conversation insights, and automation. Build and maintain feature stores, embedding pipelines, and vector-based data flows. Support RAG pipelines with structured and unstructured data ingestion. Ensure data quality, consistency, and traceability across AI workflows.

Architecture & Scalability

Design scalable, modular data architectures for high-volume environments. Optimize systems for latency, throughput, and cost, especially for AI-driven analytics. Collaborate on architectural decisions involving data storage, indexing, and lifecycle management.

Platform Development

Build services using Node.js, TypeScript, NestJS (ReactJS is a plus) Design APIs and services that expose analytics and AI outputs to frontend and product layers. Integrate with event-driven systems (e.g., Kafka) and cloud-native services. Collaborate with other engineers to ensure analytics and AI insights are usable and actionable

Product & Collaboration

Work closely with product managers and engineers to translate business questions into data products. Help define metrics, KPIs, and analytical models aligned with CCaaS product goals. Mentor peers on data-related best practices and AI-data integration patterns.

What You Bring (In Addition to Core Requirements)

Strong experience in data engineering and analytics systems. Hands-on experience with: Event streaming platforms (Kafka or similar) Analytical data modeling Large-scale data processing Experience supporting AI/ML or LLM-driven systems from a data perspective. Ability to reason about data freshness, consistency, and observability. Comfort bridging data, backend services, and product requirements.