Senior Software Engineer, Backend (AI Agent Runtime)
C
CrestaAI, Contact Center
US and CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonArtificial IntelligenceKubernetesGoDistributed Systems
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.
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.
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