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Cygnify

Backend Developer (AI)

  • Seen live on Cygnify's site 2 days ago
  • Mid level
Pay
Not stated
Location
Singapore
Open for
12+ days
First seen
Oct 23, 2026
Apply on Cygnify's site (opens in a new tab)

Posting record

No flags

Open 12+ days

The posting's record, oldest first
  1. Cygnify's site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Cygnify, so its true age is longer.

Last checked Nov 2, 2026.

How well do you match?

See how much of this posting your resume shows, and what it's missing.

Upload your resume

What the posting discloses

Not stated: Pay, real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Some employers add pay only on their own site. Check the original posting before you rule it out.

Skills mentioned

About the role

Backend Engineer, AI

Role

As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. Your work sits between models and users, where latency, correctness, reliability, and cost directly impact real-world experience.

You will build and operate production systems that turn model capability into fast, stable, observable APIs used across mobile and desktop clients.

Focus

  • Build and operate backend systems that serve AI-powered features in production.
  • Design inference pipelines, orchestration layers, and service boundaries around models.
  • Own production concerns: monitoring, logging, alerting, and incident response.
  • Optimize latency and throughput across inference, caching, batching, and streaming.

Ideal Experiences

  • Strong backend engineering fundamentals in production environments.
  • Experience running high-throughput, low-latency services.
  • Familiarity with AI inference patterns (LLMs, embeddings, multimodal).
  • Comfortable debugging distributed systems under load.
  • Bias toward shipping and learning from production behavior.

Outcomes

  • Backend systems run reliably at scale, handling production AI traffic with low latency and high throughput.
  • APIs are stable, clear, and support seamless integration with frontend and ML systems.
  • Production incidents are quickly detected, diagnosed, and resolved, minimizing user impact.
  • Iterative improvements based on real usage continuously increase system performance and reliability.

Tech Stack

  • Python
  • NodeJs
  • Pytorch
  • OpenAI / Anthropic / open-source LLMs
  • SQl & noSQL
  • Kubernetes
  • Docker

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