Sr. Backend Engineer:

Introduction

ADPCX is a challenger technology company building the next generation of AI-powered customer experience.

Backed by a group managing 27,000+ clients, we have been developing AI technology for years, combining deep operational experience with engineering, data and real-world deployment at scale.

We are building across Voice AI, conversational AI, autonomous agents, agent assist, intelligent automation and real-time analytics, designed to transform how businesses interact with their customers.

We move fast, challenge established thinking and build technology to disrupt a global industry.

We are looking for ambitious engineers and AI talent who want to build, challenge and own, not simply execute.

If you want to help build technology capable of changing an industry, ADPCX is where you can make an impact.

About the role

You'll design and own backend systems across our product suite — AI platforms, real-time services, and data-heavy applications. We're looking for an architect-minded engineer who can take a high-level idea and turn it into a system that holds up at scale. You should be comfortable in the AI space; deep voice experience is a strong plus.

What you'll do

Own backend architecture end to end — from design doc to production service

Build event-driven, distributed systems that scale with traffic and stay debuggable

Translate ambiguous product ideas into concrete system designs, then ship them

Make deliberate trade-offs on latency, throughput, cost, and failure behaviour

Set engineering standards: code review, testing, observability, documentation

Work alongside AI, infra, and product teams to move features from prototype to production

Must-have

5+ years backend engineering, strong in Python

Proven system design ability: distributed systems, queuing, caching, data modelling, API design

Hands-on experience with event-driven architecture — message brokers, streaming, async workflows (Kafka, RabbitMQ, Redis Streams, or similar)

Experience scaling production systems under real load, with real failure modes

Working familiarity with LLMs and the current AI tooling landscape

Structured engineering habits — clean code, clear interfaces, disciplined version control and reviews

Ability to own a problem end to end with minimal direction

Strong plus

Experience architecting voice systems: voice bots, real-time speech pipelines, STT/TTS integration

Scaling AI products in production — inference serving, latency tuning, cost per request

RAG pipelines and vector databases

Telephony exposure (SIP, Asterisk, FreeSWITCH, WebRTC)

Kubernetes, Docker, CI/CD, cloud infrastructure

What we look for

Someone who thinks in systems, not tickets. Who asks what happens at 10x load before writing the first line. Who cares what the code looks like six months from now.