Production AI Engineer – LLMs, RAG & Agents

Modders Consulting; Singapore; Hybrid; SGD 87,000 - 131,000; Full time

Job summary

AI platforms and assistants built on language models are developed for production, focusing on RAG pipelines, agent orchestration and business APIs. The work covers concrete topics: document ingestion, agents, and APIs with production deployment in mind.

The role emphasizes building end-to-end pipelines, integration with existing systems, and optimizing answer quality, data structuring and performance, with a strong focus on ML ops practices and vector databases.

Qualifications

• Experience in AI engineering with language models and retrieval augmented generation.
• Strong Python and orchestration framework skills (e.g., LangChain).
• Ability to build end-to-end pipelines and integrate into existing systems.
• Understanding of answer quality, data structuring and performance concerns.
• MLOps sensibility and vector databases preferred.

Responsibilities

• Designing and building specialised AI assistants, with multi-step workflow and agent orchestration.
• Designing retrieval augmented generation pipelines: document ingestion, chunking, embeddings, ranking and retrieval.
• Continuously improving answer quality and running tool/e model evaluation.
• Building APIs that expose assistants to business applications.
• Monitoring usage and keeping performance and inference costs under control.

Skills

AI engineering

Python

LangChain

LLM/RAG concepts

Pipeline design

Performance optimization

Tools

LangGraph

RAG pipelines

FastAPI

PostgreSQL/pgvector

Docker

Kubernetes

Azure AI

CI/CD

Job description

AI platforms and assistants built on language models are developed for production, focusing on RAG pipelines, agent orchestration and business APIs. The work covers concrete topics: document ingestion, agents, and APIs with production deployment in mind.

The role emphasizes building end-to-end pipelines, integration with existing systems, and optimizing answer quality, data structuring and performance, with a strong focus on ML ops practices and vector databases.