About this opportunity
Telnyx lists this Senior Software Engineer, Python opportunity in remote, Oregon. Review the employer’s description below for duties, qualifications and application requirements.
Job description
About Telnyx
Telnyx is an industry leader thats not just imagining the future of global connectivity—were building it. From architecting and amplifying the reach of a private, global, multi-cloud IP network , to bringing hyperlocal edge technology right to your fingertips through intuitive APIs, were shaping a new era of seamless interconnection between people, devices, and applications.
Were driven by a desire to transform and modernize whats antiquated, automate the manual, and solve real-world problems through innovative connectivity solutions. As a testament to our success, were proud to stand as a financially stable and profitable company. Our robust profitability allows us not only to invest in pioneering technologies but also to foster an environment of continuous learning and growth for our team.
Our collective vision is a world where borderless connectivity fuels limitless innovation. By joining us, you can be part of laying the foundations for this interconnected future. Were currently seeking passionate individuals who are excited about the opportunity to contribute to an industry-shaping company while growing their own skills and careers.
The Role
Were looking for a senior engineer who lives at the seam between software, operations and AI. Youve built systems where an order POST doesnt end at a database row — it ends at a carrier activating a real phone number in another country, subject to that countrys rules. And increasingly, it ends with an AI agent deciding — reliably, auditable, in milliseconds — whether that order flows straight through or needs a human.
Youll join the team that owns Telnyxs phone-number inventory and the entire ordering pipeline behind it. The stack is Python, the domain is global telephony, and the roadmap is near-total automation: if a step in the pipeline is done by a human today, were building the system that takes that step away.
What Youll Own
* The ordering pipeline — async Python services that turn a search-and-order API call into a provisioned, billable phone number: job queues, retries, rollbacks on partial failure, SLA clocks, and backorder (advanced order) fulfillment where lead-time promises have contractual weight. Tens of thousands of orders a week run through this today.
* Vendor & carrier integrations — dozens of them, from US Tier-1 wholesale carriers to international carriers and regulated-market vendors. Each integration is its own Snowflake — idiosyncratic APIs, failure modes, and regulatory constraints. Youll make them behave like one system.
* The AI order-review layer — a production LLM service that reads orders and customer-submitted regulatory documents and auto-approves by country, number type, and carrier, governed by allowlists, score thresholds, and full audit logs. Youll own expanding its coverage safely: evals, guardrails, regression suites, and clean handoffs to humans when the machine isnt sure.
* Regulatory automation — per-country identity/documentation checks ("requirement groups"), external document-verification vendors, expiry and re-verification flows — automated end-to-end in 45+ countries we hold carrier licences in.
* The data layer — a large Postgres-based warehouse and Metabase analytics serving stock, SLA, and order-lifecycle dashboards used daily by ops and leadership, across a 30+ microservice estate; alerting on stock risk, vendor SLA breaches, and pipeline health.
Tech stack (what youll actually touch)
* Python 3 (asyncio, FastAPI/aio SQLAlchemy, Alembic, pydantic), pytest, mypy/ruff
* LLM tooling: model APIs, structured output, agent orchestration, prompt/scoring configuration, offline eval harnesses
* PostgreSQL at scale: schema design, migrations on live systems, query tuning, warehouse SQL
* RabbitMQ/Kafka, Consul, Kubernetes, Jenkins, Docker/OCI, GitHub Actions
* Metabase, and TypeScript/Next.js for internal dashboards (not required — the backend is yours)
What were looking for
* 5+ years of backend engineering, with strong production async Python.
* Youve owned systems that talk to many external services and lived with the async-job, retry, rollback, and eventual-consistency problems that creates.
* You can make an LLM trustworthy in a real flow: validation, scoring thresholds, evals, audit, and human-in-the-escalation. You know where AI is reliable and where its theater.
* Postgres beyond ORM basics — you can read a plan, write a migration on a live system, and know why SKIP LOCKED matters.
* Youre a pragmatist about process automation — a workflow isnt done when the ticket closes, its done when the human step is gone and the numbers prove it.
* Strong written communication across a distributed team (EMEA + Americas).
Bonus points for
* Telecom/CPaaS background: DID inventory, ordering, porting (NPAC), carrier provisioning, regulatory/KYC familiarity.
* Experience with document/KYC verification vendors (Onfido, Sumsub, Jumio) or OCR pipelines.
* Building with agents beyond work: MCP servers, tool-calling frameworks, open-source agent runtimes.
* Warehouse/analytics engineering; internal-dashboard work in TypeScript.
What this role is not
* Not a pure CRUD role. Every row in this system is a real phone number somewhere — on a carrier, under a regulator, promised to a customer, watched by an SLA.
* Not an ML research role. Youll build applied AI that approves real transactions — no notebooks in production, no fine-tuning required.
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.