Backend Developer (AI)

Cygnify

Singapore

Who this role is best for

Mid-level backend engineers with AI inference experience and production system optimization skills will find this role in Singapore aligns with their technical focus on scalable, low-latency systems.

Best fit for

Mid-level engineers with experience in AI inference and production system optimization
— “Build and operate backend systems that serve AI-powered features in production.”
Candidates comfortable with distributed systems and incident response in high-throughput environments
— “Comfortable debugging distributed systems under load.”
Individuals with strong background in Python, Pytorch, and cloud-native technologies like Kubernetes
— “Tech Stack: Python, NodeJs, Pytorch, OpenAI / Anthropic / open-source LLMs, SQl & noSQL, Kubernetes, Docker”

Things to consider

The role requires direct ownership of production concerns and incident resolution
— “Own production concerns: monitoring, logging, alerting, and incident response.”
Candidates must be prepared to work with multiple AI model providers and frameworks
— “OpenAI / Anthropic / open-source LLMs”

How to stand out

Highlight experience with AI inference pipelines and model orchestration in your resume and interviews
— “Design inference pipelines, orchestration layers, and service boundaries around models.”
Emphasize your track record in optimizing latency and throughput in production systems
— “Optimize latency and throughput across inference, caching, batching, and streaming.”
Showcase your ability to build and operate reliable backend systems at scale
— “Backend systems run reliably at scale, handling production AI traffic with low latency and high throughput.”

Pace · Fast PacedCollaboration · MediumAutonomy · MediumDecision Impact · TeamLevel · Senior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

reliable backend systems
low latency
high throughput
stable APIs
quick incident resolution

Typical background

strong backend engineering experienceAI and machine learning

Skills & requirements

Required

Backend EngineeringProduction EnvironmentsHigh-throughput ServicesAI Inference PatternsDistributed Systems

Preferred

LlmsEmbeddingsMultimodal

Stack & domain

PythonNode.jsPyTorchOpenaiAnthropicSQLNoSQLKubernetesDockerAIBackend Engineering

About the role

Original posting from Cygnify via Ashby

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

Source: Cygnify careers (Ashby)