Senior AI Engineer - Production ML & LLM Systems

PT Graha Karya Informasi; Jakarta Utara; On-site; IDR 150,000,000 - 300,000,000; Full time

Job summary

Graha Karya Informasi is seeking a skilled Engineer with strong Python/ML experience to join the team in Jakarta. Your role centers on deploying production-grade AI/ML systems, leveraging LLMs, RAG architectures, and vector databases.

You will work across API development, microservices, and cloud platforms to enable real-time inference and observability. The ideal candidate has 3+ years of engineering experience, proficiency across the AI stack, and a hands-on approach to turning POC concepts

Qualifications

• Min 3 years experience as Engineer.
• Strong Python + ML ecosystem proficiency.
• Proven production AI/ML deployments.
• Hands-on with LLMs and RAG architectures.
• Experience with vector databases (FAISS, Qdrant, Azure AI Search).
• Software engineering fundamentals: API, microservices, distributed systems.
• Experience with Docker and Kubernetes in production.
• Cloud platforms (GCP preferred; AWS/Azure welcome).
• CI/CD, testing, model lifecycle, and observability.
• Real-time processing (streaming, queues, events).

Responsibilities

• Convert POC into production systems.
• Deploy and maintain production-grade AI/ML services.
• Collaborate with stakeholders to explain technical concepts.

Skills

Python

ML ecosystem

LLMs

RAG architecture

Vector databases

API development

Microservices

Docker

Kubernetes

Cloud platforms

CI/CD

Observability

Real-time processing

Tools

Docker

Kubernetes

GCP

AWS

Azure

FAISS

Qdrant

Job description

Graha Karya Informasi is seeking a skilled Engineer with strong Python/ML experience to join the team in Jakarta. Your role centers on deploying production-grade AI/ML systems, leveraging LLMs, RAG architectures, and vector databases.

You will work across API development, microservices, and cloud platforms to enable real-time inference and observability. The ideal candidate has 3+ years of engineering experience, proficiency across the AI stack, and a hands-on approach to turning POC concepts