Machine Learning / AI Engineer

GMP RECRUITMENT SERVICES (S) PTE LTD; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

GMP RECRUITMENT SERVICES (S) PTE LTD is seeking a skilled engineer to build and operate end-to-end ML/AI systems in production. The role covers data prep, model training, inference, deployment, and ongoing iteration, with a focus on scalable backend services and AI infrastructure.

You will work with Python/Node.js stacks, Docker/Kubernetes, and LLM-based tooling to design robust pipelines, monitoring, and optimization across distributed systems. Singapore-based applicants preferred.

Qualifications

• Experience building and operating high-throughput, low-latency backend services.
• Hands-on experience developing and deploying ML/AI systems, including model training, evaluation, inference, and production monitoring.
• Experience with LLM-based systems and AI inference patterns, including OpenAI, Anthropic, or open-source LLMs, embeddings, tool calling, agent workflows, memory, or multimodal AI.
• Proficiency in Python and Node.js, with experience using SQL and/or NoSQL databases, Docker, and Kubernetes.
• Experience debugging and optimizing distributed AI systems in production, including inference latency, throughput, caching, batching, streaming, observability, reliability, fallback mechanisms, and resource/cost optimization.

Responsibilities

• Build and operate end-to-end ML and AI systems covering data preparation, model training, evaluation, inference, deployment, and production iteration.
• Develop production-grade AI infrastructure and backend services, including inference pipelines, orchestration layers, model-serving services, APIs, caching, batching, and streaming.
• Design and implement agentic AI workflows supporting multi-step planning, tool use, memory, failure handling, recovery, and integration with LLMs and external systems.
• Monitor, debug, and optimize AI systems in production using logging, metrics, tracing, alerting, and production signals to improve latency, throughput, cost, reliability, and safety.
• Develop and integrate AI-powered product features across frontend, backend, ML, and AI systems, using production performance and usage data to drive continuous improvements.

Skills

Python

Node.js

Data structures & backend design

Performance optimization

Tools

Docker

Kubernetes

PyTorch

LLMs

Embeddings

Tool calling

Job description

Responsibilities

• Build and operate end-to-end ML and AI systems covering data preparation, model training, evaluation, inference, deployment, and production iteration.
• Develop production-grade AI infrastructure and backend services, including inference pipelines, orchestration layers, model-serving services, APIs, caching, batching, and streaming.
• Design and implement agentic AI workflows supporting multi-step planning, tool use, memory, failure handling, recovery, and integration with LLMs and external systems.
• Monitor, debug, and optimize AI systems in production using logging, metrics, tracing, alerting, and production signals to improve latency, throughput, cost, reliability, and safety.
• Develop and integrate AI-powered product features across frontend, backend, ML, and AI systems, using production performance and usage data to drive continuous improvements.

Requirements

• Strong production software engineering fundamentals, with experience building and operating high-throughput, low-latency backend services.
• Hands‑on experience developing and deploying ML or AI systems, including model training, evaluation, inference, and production monitoring; experience with PyTorch .
• Experience with LLM-based systems and AI inference patterns, including OpenAI, Anthropic, or open-source LLMs , embeddings, tool calling, agent workflows, memory, or multimodal AI.
• Proficiency in Python and Node.js , with experience using SQL and/or NoSQL databases , Docker , and Kubernetes .
• Experience debugging and optimizing distributed AI systems in production, including inference latency, throughput, caching, batching, streaming, observability, reliability, fallback mechanisms, and resource/cost optimization.