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Open nowFirst seen 15 hours ago

Backend Engineer, AI (Agent Systems)

Hyphen Connect71 open roles

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Your applicationOpen nowBackend Engineer, AI (Agent Systems)Hyphen Connect

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.0%1 day
  2. 4.3%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: first seen 15 hours ago

The posting

Our Client is a AI start-up company. As a Backend Engineer, AI, you'll build and operate the backend infrastructure that powers every AI interaction within the product. You'll design and maintain production-grade systems that transform model capabilities into robust APIs and services consumed by mobile and desktop applications, with a strong focus on performance, observability, and operational excellence.

Responsibilities

  • Build, deploy, and maintain backend services that power AI features in production.
  • Design inference pipelines, orchesation layers, and service architectures around AI models.
  • Develop scalable APIs that enable seamless integration between AI models, frontend applications, and internal services.
  • Own production operations, including monitoring, logging, alerting, and incident response.
  • Optimize inference performance through caching, batching, streaming, and other latency-reduction techniques.
  • Improve system scalability, reliability, and cost efficiency based on production metrics and usage patterns.
  • Collaborate closely with machine learning, product, and frontend teams to deliver high-quality AI-powered experiences.

Requirements

  • Strong backend engineering experience building and operating production systems.
  • Experience developing high-throughput, low-latency distributed services.
  • Familiarity with AI inference architectures, including LLMs, embeddings, and multimodal models.
  • Strong debugging and troubleshooting skills in distributed systems and production environments.
  • Experience designing scalable APIs and backend services.
  • A pragmatic, ownership-driven mindset with a focus on shipping reliable systems and continuously improving them through production insights.
  • Technologies and tools: Python, Node.js, PyTorch, OpenAI, Anthropic, and open-source LLMs, SQL & NoSQL databases, Kubernetes, Docker

Success Looks Like

  • AI backend services consistently deliver low-latency, high-throughput performance at production scale.
  • APIs are reliable, well-designed, and enable seamless integration across frontend and machine learning systems.
  • Production issues are detected early, resolved quickly, and followed by meaningful reliability improvements.
  • Inference infrastructure is scalable, observable, and cost-efficient as usage grows.
  • Continuous improvements driven by production data result in measurable gains in system performance, stability, and developer experience.
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