Software Engineer, AI - Python
About this role
The RevOps team at Telnyx builds the systems layer, operations, and automation supporting the company's growth engine, moving from human-facing GTM tools toward AI agents and automation that interact directly with the stack. As a Software Engineer specializing in AI, you will design and operate the AI-native backend systems powering this go-to-market motion, including multi-agent architectures, reliable integrations across complex business systems, and end-to-end services from prototype to production.
This role exists to support both human-facing workflows and bot-facing infrastructure through clean data, reliable integrations, durable automations, and scalable operating patterns. An ideal candidate is a hands-on builder with high ownership who makes architectural decisions, ships iteratively, debugs production issues, and treats AI as a core primitive for systems engineering rather than simple prompt engineering.
What you will do
- Design and build multi-agent AI systems in Python that handle complex, multi-step business workflows such as 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 using FastAPI or Flask deployed on Kubernetes with CI/CD, managing the full lifecycle from deployment configuration to production reliability.
- Design tool-use patterns for AI agents including structured function calling, multi-step reasoning, state management across conversation turns, and graceful handling of model failures.
- Build integrations across external systems like CRM, enrichment APIs, outreach platforms, and Slack with proper error handling, retries, rate limiting, and data contracts.
- Instrument and monitor AI systems in production by building observability into agent behavior, tracking success rates, detecting regressions, and debugging non-deterministic failures.
- Design and run experiments such as A/B tests, prompt variations, and model comparisons with proper evaluation infrastructure to measure what is actually working.
What you need
- Have at least 2 years of software engineering experience building backend services in Python.
- Demonstrate production experience building multi-step AI agent systems involving stateful workflows where models make decisions, call tools, and operate across multiple turns.
- Show a strong understanding of LLM internals affecting system design, including context window management, token budgets, cost, latency, capability tradeoffs, and hallucination strategies.
- Bring experience testing and evaluating non-deterministic AI systems with alternatives to standard assertions.
- Possess solid software architecture fundamentals covering API design, state management, fault tolerance, and graceful degradation.
- Have production experience with containerized deployments using Docker, Kubernetes, and CI/CD pipelines.
- Demonstrate experience integrating with external APIs at scale alongside proficiency with SQL and data systems.
- Exhibit high ownership by deploying your own code, investigating incidents, and closing the loop between shipped work and performance.
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
Skills and tech stack
Questions candidates ask
Where is the position located?
The role is available in the United States, Brazil, and Canada.
What kind of systems will I be building?
You will design and operate AI-native backend systems powering the go-to-market motion, including stateful, multi-step agent systems running on Kubernetes that make decisions, call tools, and interact with external APIs.
What are the core technical requirements for the role?
Requirements include 2+ years of software engineering experience building backend services in Python, production experience with multi-step AI agent systems, containerized deployments with Docker and Kubernetes, and solid software architecture fundamentals.