Backend Engineer, AI

TechKnowledgey Pte Ltd; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

TechKnowledgey Pte Ltd is seeking a backend-focused engineer to build and scale the infrastructure behind AI-driven products. The role spans application services, model integration, and distributed systems with a focus on reliable production software.

You will turn AI capabilities into dependable backend services that support real-world usage across multiple product surfaces. The position emphasizes delivering and iterating quickly within a distributed, production-focused environment.

Qualifications

• Proven backend development experience with scalable services.
• Strong fundamentals in software engineering.
• Experience with AI/ML systems, especially LLM-based apps and embeddings.
• Experience with distributed architectures and production troubleshooting.
• Hands-on approach prioritizing fast delivery and iterative improvements.
• Ability to balance quality, performance, scalability, and ops considerations.

Responsibilities

• Develop and maintain backend services supporting AI-enabled applications.
• Build architectures and workflows around machine learning models.
• Integrate LLMs, embeddings, and other AI capabilities.
• Improve system performance via caching, batching, async processing, and streaming.
• Establish monitoring, logging, alerting, and operational practices.
• Troubleshoot issues across distributed services and production.
• Collaborate with engineering and AI teams to ship capabilities to production.

Skills

Backend development

Distributed systems

AI/ML integration

Performance optimization

Tools

Python

Node.js

PyTorch

Kubernetes

Docker

SQL/NoSQL databases

Cloud platforms

Job description

We’re looking for a backend-focused engineer to help build and scale the infrastructure behind AI-driven products. The role involves working across application services, model integration, and distributed systems, with a strong emphasis on building reliable production software.

You’ll be responsible for turning AI capabilities into dependable backend services that can support real-world usage across multiple product surfaces.

Sounds great – what will I do?

• Develop and maintain backend services supporting AI-enabled applications.

• Build service architectures and processing workflows around machine learning models.

• Integrate and manage interactions with LLMs, embeddings, and other AI capabilities.

• Improve system performance through techniques such as caching, batching, asynchronous processing, and streaming.

• Establish and maintain effective monitoring, logging, alerting, and operational practices.

• Troubleshoot complex issues across distributed services and production environments.

• Work closely with engineering and AI/ML teams to bring new capabilities from development into production.

Sounds perfect to me, what specifics are you looking for?

• Strong software engineering fundamentals with solid backend development experience.

• Experience building scalable services where performance and reliability are important.

• Exposure to AI/ML systems, particularly LLM-based applications, inference workflows, embeddings, or multimodal technologies.

• Comfortable working with distributed architectures and diagnosing issues in production.

• Practical, hands-on approach to engineering with an emphasis on delivering and iterating quickly.

• Ability to balance engineering quality, performance, scalability, and operational considerations.

What Success Looks Like

• Backend services remain stable and performant as AI workloads grow.

• AI capabilities can be exposed through well-designed, maintainable APIs and services.

• Production issues are identified and resolved efficiently with minimal disruption.

• System performance, scalability, and reliability improve continuously through measurement and iteration.

• New AI capabilities can be integrated into the product without creating unnecessary operational complexity.

Technical proficiency across:

• Python

• Node.js

• PyTorch

• Commercial and open-source LLM platforms

• SQL and NoSQL databases

• Kubernetes

• Docker

• Cloud-based infrastructure and distributed services