Senior AI Platform Engineer — Build Scalable LLM Services

NCS; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

NCS is seeking an AI Platform Engineer to join its AI Centre of Excellence. You will build and operate production-grade AI platform components, APIs, and workflows, enabling internal teams to access LLM capabilities reliably.

You will own end-to-end platform capabilities, work with Python, Kubernetes, vector databases, and RAG pipelines, and drive performance, scalability, and usability for developers across the organization.

Qualifications

• 6–8 years of software engineering experience.
• 1–2 years hands-on experience with AI or LLM-based systems
• Proven track record building backend services in production environments
• Experience with shared or platform-based architectures
• Familiarity with distributed systems and service-oriented design

Responsibilities

• Build AI platform services and APIs using Python.
• Integrate with model access layers, data systems, and platform services.
• Contribute to shared libraries and reusable platform components.
• Implement AI workflows including RAG pipelines and inference services.
• Develop LLM integration patterns and prompt-based workflows.
• Work with vector databases and embedding pipelines.
• Design services for reliability, scalability, and fault tolerance.
• Optimize latency, throughput, and resource utilization.
• Support deployment, testing, and production operations.
• Improve platform usability for internal developers.

Skills

Backend services

Distributed systems

Platform architectures

RAG concepts

Vector databases

Model serving

Containerization

CI/CD pipelines

Version control

Monitoring/logging

Ownership mindset

Clear communication

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Python

Kubernetes

Git

CI/CD tooling

Job description

NCS is seeking an AI Platform Engineer to join its AI Centre of Excellence. You will build and operate production-grade AI platform components, APIs, and workflows, enabling internal teams to access LLM capabilities reliably.

You will own end-to-end platform capabilities, work with Python, Kubernetes, vector databases, and RAG pipelines, and drive performance, scalability, and usability for developers across the organization.