GenAI Solutions Architect: Azure AI, LLMs & RAG

Systems Limited; Malaysia; On-site; MYR 180,000 - 360,000; Full time

Job summary

Systems Limited is seeking a hands-on AI Architect to lead enterprise GenAI initiatives from concept to production. You will design, build, and deliver scalable AI solutions while working closely with business stakeholders.

The role requires expertise in LLMs, RAG, Azure AI services, governance, and evaluation frameworks, with a track record of deploying AI in cloud environments and mentoring engineers.

Qualifications

• 8+ years of AI/ML engineering and solution architecture.
• 3+ years hands-on experience building production-grade GenAI and LLM apps.
• Strong Python, FastAPI, Azure AI services, and NLP fundamentals.
• Experience with AI agents and evaluation metrics.
• Experience deploying AI on Azure with CI/CD and hybrid architectures.
• Bachelor's in CS/AI/Data Science; Master’s preferred.

Responsibilities

• Lead end-to-end design, development, and deployment of AI and GenAI solutions.
• Architect scalable, secure AI systems using Azure AI services.
• Collaborate with business and client stakeholders to translate requirements into AI solutions.
• Build and optimize LLM-powered applications, RAG pipelines, APIs, and workflows.
• Mentor junior engineers and ensure solution quality.
• Implement AI evaluation, observability, guardrails, and responsible AI practices.
• Deploy AI applications with CI/CD, monitoring, and cloud infra.

Skills

AI/ML engineering

Solution architecture

Python

FastAPI

Azure AI services

RAG

LLM orchestration

NLP

Prompt engineering

AI governance

Education

Bachelor's degree in CS/AI/Data Science

Master's degree preferred

Tools

LangGraph

AutoGen

Semantic Kernel

CrewAI

LangSmith

Langfuse

Azure AI Foundry

HuggingFace

Vector databases

Job description

Systems Limited is seeking a hands-on AI Architect to lead enterprise GenAI initiatives from concept to production. You will design, build, and deliver scalable AI solutions while working closely with business stakeholders.

The role requires expertise in LLMs, RAG, Azure AI services, governance, and evaluation frameworks, with a track record of deploying AI in cloud environments and mentoring engineers.