Senior Software Engineer, Backend (AI Agent Runtime)

Cresta builds an AI platform that helps companies improve customer experience through conversational AI agents, human agent augmentation, and conversation intelligence. The Senior Software Engineer, Backend role focuses on building real-time AI agent infrastructure, distributed runtime systems, function execution platforms, and developer tooling while driving production quality and operational excellence.

Responsibilities:

• Build real-time AI agent infrastructure: Design and operate the stateful, low-latency runtime that powers voice and chat AI agents — from LLM streaming and conversation state management to graceful recovery and multi-channel support
• Solve distributed systems problems: Own session management across scaled-out workers — including affinity, checkpointing, crash recovery, and consistency under concurrent access
• Build a function execution platform: Own a serverless-style runtime where customers deploy custom logic — build orchestration, container lifecycle, autoscaling, and versioned rollouts
• Own developer experience and test infrastructure: Build CLI tools, local development environments, and test execution frameworks that let engineers iterate quickly and ship with confidence
• Raise the bar on production quality: Drive observability, incident response, and engineering best practices across the team

Requirements:

• 5+ years of software engineering experience, with meaningful time spent on infrastructure, platform, or systems work
• Strong Python and Go — both are core to this role, not one primary and one secondary
• Deep understanding of distributed systems: consistency, fault tolerance, state management, concurrency
• Experience with Kubernetes and cloud-native infrastructure
• Experience building developer-facing tooling — CLIs, SDKs, local dev environments, or internal platforms
• Strong communicator who can drive technical decisions, write clear design docs, and mentor others
• High bar for code quality — thorough testing, thoughtful code review, and sustainable engineering practices
• Comfort operating what you build — on-call, incident response, and production ownership
• AI-native workflow — you actively use LLMs and AI-assisted tools in your daily development, and can leverage them to move faster and tackle problems that would otherwise be impractical
• Experience with real-time voice or streaming media systems
• Hands-on with LLM integration — streaming inference, prompt orchestration, retrieval-augmented generation
• Experience building serverless or function-as-a-service platforms
• Workflow engines (Temporal, Argo, Airflow) for durable, long-running processes
• Experience in conversational AI or speech domains
• Infrastructure-as-code and GitOps workflows