Full Stack Engineer
Structurely — Canada · Posted ~1 day ago
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Company Description
We’re building an agentic call center platform that powers real customer conversations and automates operating models across industries.
Our proprietary AI Telco platform carries out conversations using Voice and Text AI across multiple communication channels.
Our backend orchestrates multiple vendors for response generation, speech to text, and text to speech.
It also acts as the glue between our platform, numerous CRMs, and various telecom providers.
We move fast, operate with high trust, and value engineers who take real ownership and ship.
The Opportunity
We need a senior backend engineer with high agency to own and evolve our backend services.
You’ll build reliable, scalable systems that support real-time, high-throughput workloads (telephony + AI inference + integrations).
Python is the primary language today, but we want someone excited to work in TypeScript as well, especially where it’s the right tool for the job.
You’ll help us scale on our current AWS container stack (ECS + Docker) while contributing to our migration toward Kubernetes.
Role Description
What You’ll Do - Own backend services end-to-end
Take primary ownership of key backend domains/services (design, implementation, deployment, uptime, iteration).
Write clear technical plans, make pragmatic tradeoffs, and drive work to completion without needing heavy process.
Establish and improve service-level standards: reliability, performance, observability, and maintainability.
Develop APIs and services that orchestrate agent workflows across LLM/TTS/STT vendors.
Design resilient patterns for retries, timeouts, fallbacks, rate limits, and circuit breaking across external providers.
Build capabilities that support real-time and near-real-time workloads; telephony latency constraints, event-driven processing.
Build and maintain integrations with multiple CRMs; bi-directional sync, webhooks, workflow triggers, identity mapping.
Support telecom integrations; events, call state, messaging, routing, configuration, ensuring reliability and traceability.
Create internal abstractions/adapters so integrations are consistent, testable, and extensible.
Model and operate systems using MongoDB as the primary datastore.
Use Aurora (RDS) where relational guarantees are required.
Use RabbitMQ for asynchronous workflows and background processing.
Use Redis for caching, ephemeral state, rate limiting, and coordination where appropriate.
Ship on Docker + ECS today; contribute materially to migration planning/execution toward Kubernetes.
Improve CI/CD, environment parity, and deployment safety; rollbacks, progressive delivery, migration strategies.
Partner with security/compliance needs; secrets management, auditability, least privilege, data retention.
Our Tech Stack (you don’t need all of this)
Languages: Python (primary), TypeScript (growing footprint)
Containers/Runtime: Docker, AWS ECS (current), Kubernetes (evolution)
Datastores: MongoDB (primary), AWS Aurora (RDS)
Messaging/Cache: RabbitMQ, Redis
Cloud: AWS networking, IAM, logs/metrics, container registry, etc.
Domain: Agentic orchestration, LLM/TTS/STT vendor integrations, CRM integrations, telecom integrations
Qualifications
Experience with telecom/telco systems (call routing, SIP/telephony infrastructure, messaging, carrier integrations).
7–10+ years of backend engineering experience with demonstrated end-to-end ownership of production services.
Strong proficiency in Python and solid engineering fundamentals APIs, data modeling, distributed systems basics.
Willingness and interest in contributing in TypeScript or strong desire to learn quickly.
Experience shipping and operating containerized services with Docker and a production orchestrator (ECS and/or Kubernetes).
Practical experience with MongoDB and/or relational databases Aurora/Postgres, including schema design and performance tuning.
Experience with messaging systems RabbitMQ or similar and caching Redis, including failure modes and operational concerns.
Strong debugging skills and an operator mindset: observability, incident response, and continuous reliability improvements.
High agency: you take ambiguous problems, turn them into plans, align stakeholders, and deliver.
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