Software Engineer, GTM AI - Python

About Telnyx

Telnyx is an industry leader that's not just imagining the future of global connectivity—we're 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, we're shaping a new era of seamless interconnection between people, devices, and applications.

We're driven by a desire to transform and modernize what's antiquated, automate the manual, and solve real-world problems through innovative connectivity solutions. As a testament to our success, we're 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. We're currently seeking passionate individuals who are excited about the opportunity to contribute to an industry-shaping company while growing their own skills and careers.

About the Team

The RevOps team owns the systems layer, operations & automation that supports Telnyx's growth engine. Historically, that meant administering GTM tools used by humans: Salesforce, marketing automation, enrichment vendors, routing, campaign workflows, reporting, and vendor integrations.

That operating model is changing. Telnyx is increasingly building AI agents and automation that interact directly with the GTM stack. The systems team now needs to support both human-facing workflows and bot-facing infrastructure: clean data, reliable integrations, durable automations, documented process, and scalable operating patterns.

About the Role

We're looking for a Software Engineer who builds and operates the AI-native backend systems powering our go-to-market motion. You'll design multi-agent architectures, build reliable integrations across complex business systems, and own services end-to-end from prototype through production.

The systems you build orchestrate LLM-powered agents that handle real business workflows — qualifying leads, generating emails, routing meetings, enriching contacts, and managing outbound campaigns. These are stateful, multi-step agent systems running on Kubernetes that make decisions, call tools, and interact with external APIs under real constraints: rate limits, token budgets, cost targets, and data quality issues.

You'll partner with Engineering Leads and Technical Product Managers to understand the problem space, then translate those problems into well-architected, observable, and maintainable software. This isn't prompt engineering and it isn't gluing together SaaS tools - it's systems engineering with AI as a core primitive.

This is a hands-on builder role with high ownership. You'll make architectural decisions, ship iteratively, debug production issues, and care deeply about what happens after code merges.

Responsibilities

Design and build multi-agent AI systems in Python that handle complex, multi-step business workflows - qualification, email generation, routing, enrichment, and outbound orchestration

Architect model-agnostic abstraction layers that decouple business logic from LLM providers, enabling flexibility across Claude, GPT, and open-source models

Build and operate backend services (FastAPI/Flask) deployed on Kubernetes with CI/CD, managing the full lifecycle from deployment configuration to production reliability

Design tool-use patterns for AI agents - structured function calling, multi-step reasoning, state management across conversation turns, and graceful handling of model failures

Build integrations across external systems (CRM, enrichment APIs, outreach platforms, Slack) with proper error handling, retries, rate limiting, and data contracts

Instrument and monitor AI systems in production — build observability into agent behavior, track success rates, detect regressions, and debug non-deterministic failures

Design and run experiments (A/B tests, prompt variations, model comparisons) with proper evaluation infrastructure to measure what's actually working

Requirements

2+ years of software engineering experience building backend services in Python

Production experience building multi-step AI agent systems — stateful workflows where models make decisions, call tools, and operate across multiple turns, not single-shot API wrappers

Strong understanding of LLM internals as they affect system design: context window management, token budgets, cost/latency/capability tradeoffs across models, structured outputs, and strategies for handling hallucination and refusals

Experience testing and evaluating non-deterministic AI systems — you understand that assert output == expected doesn't work and have built or used alternatives

Solid software architecture fundamentals: API design, state management, fault tolerance, and graceful degradation when upstream services fail

Production experience with containerized deployments (Docker, Kubernetes) and CI/CD pipelines

Experience integrating with external APIs at scale — auth flows, rate limiting, retries, data normalization, and managing the operational complexity of multiple third-party dependencies

Proficiency with SQL and data systems for building targeting, enrichment, and analytics pipelines

Built observability into production systems — structured logging, tracing, alerting, and monitoring that you actually use to debug issues

High ownership: you deploy your own code, investigate your own incidents, and close the loop between what you shipped and how it performs

Nice to Have

Experience with specific GTM/RevOps systems (Salesforce, Apollo, Lusha, enrichment providers) or similar complex business platforms

Background in growth engineering, marketing automation, or revenue operations tooling

Experience with Slack bot development or conversational AI interfaces

Contributions to or experience with open-source AI agent frameworks

Familiarity with ArgoCD, StatefulSets, or Kubernetes operations beyond basic deployments