Kerja Sepenuh Masa, Full Stack AI Engineer di RSGx Federal Territory - Maukerja

About The Role

We are looking for a Full Stack AI Engineer to design, build, and operate AI-powered applications that solve real business problems — in production, not demos. This role combines production-grade software engineering with modern AI capabilities: LLMs, RAG pipelines, AI agents, and secure cloud deployment.

You will own features end-to-end, from requirements gathering and architecture through to deployment, monitoring, and iteration. You will work closely with product teams, business stakeholders, and developers to ship AI solutions that are reliable, observable, cost-efficient, and secure.

This is a production engineering role. If something you build fails at 2am, you understand why — and you have already built the observability to detect it earlier next time.

What You'll Do AI & LLM Engineering

• Design and build LLM-powered applications using models such as Claude, GPT-4, Gemini, or open-source equivalents (Llama, Mistral), selecting the right model based on latency, cost, and quality tradeoffs.
• Build and optimise RAG pipelines connecting LLMs to enterprise knowledge sources; own chunking strategy, retrieval quality, and embedding model selection.
• Develop AI agents and multi-agent systems using frameworks such as LangChain, LangGraph, CrewAI, or LlamaIndex; design for the failure modes specific to agentic workflows.
• Build evaluation harnesses that measure output quality, faithfulness, hallucination rate, latency, and cost across model versions and prompt changes — governing whether AI features ship or roll back.
• Implement prompt engineering at the system level, including guardrails, output filtering, and red-teaming to ensure safe and reliable AI behaviour.
• Integrate workflow automation platforms (e.g. n8n, Make, Power Automate) to extend AI capabilities into business operations.
  
  

Full Stack Development

• Build responsive front-end interfaces using React, Next.js, or Vue.js with TypeScript.
• Develop backend services, APIs, and microservices in Python (FastAPI/Django) or Node.js; design secure RESTful and GraphQL APIs with robust authentication and authorisation (OAuth 2.0, JWT).
• Integrate AI systems with existing business platforms and third-party services.
• Design and maintain scalable databases and data pipelines; implement encryption at rest and in transit, secure data storage, and strict access controls.
• Conduct threat modelling and secure code reviews; integrate SAST/DAST tooling into CI/CD pipelines to catch vulnerabilities before deployment.
  
  

Cloud, DevOps & Security

• Deploy and operate AI applications on Microsoft Azure (essential); experience with AWS or GCP is advantageous. Apply Azure-native ML services including Azure OpenAI Service, Azure Machine Learning, and Azure AI Foundry.
• Build and maintain CI/CD pipelines incorporating security scanning, secrets management, and automated testing.
• Manage containerised workloads using Docker and Kubernetes; establish IAM guardrails and infrastructure-as-code practices.
• Monitor application performance, AI model behaviour, and inference costs in production; own incident response including root cause analysis and corrective action.
  
  

Collaboration & Delivery

• Gather and translate stakeholder requirements into scoped technical solutions; push back constructively when a proposed approach adds complexity without delivering value.
• Work across product, data, and business teams to deliver projects on time.
• Stay current with the evolving AI engineering landscape and bring forward practical recommendations.
  
  

What We're Looking For Education & Experience

• Bachelor's Degree in Computer Science, Software Engineering, Data Science, or a related field.
• 3+ years of software development experience in a production environment.
• Demonstrable hands-on experience with Microsoft Azure — including compute, networking, IAM, and Azure-native AI services (Azure OpenAI, Azure ML, Azure AI Foundry).
• 1+ year of hands-on experience building and deploying AI/LLM applications.
  
  

Technical Skills — Core

Preferred Qualifications

• Experience building and operating AI agent and multi-agent systems.
• Experience integrating AI into enterprise applications at scale.
• Knowledge of MLOps practices: model versioning, monitoring, drift detection.
• Deep experience with Azure AI services: Azure OpenAI Service, Azure Machine Learning, Azure AI Foundry, and Azure Cognitive Services.
• Familiarity with workflow automation tools: n8n, Make, Zapier, or Power Automate.
• Exposure to data engineering and ETL pipelines.
• Familiarity with AI safety practices: output guardrails, adversarial testing, LLM-as-judge evaluation.
  
  

What Strong Candidates Demonstrate Beyond credentials, we look for engineers who can:

• Describe a specific production failure in an AI system they owned — the failure mode, and what they built to prevent it recurring.
• Walk through a RAG pipeline they shipped: retrieval quality before and after a meaningful improvement, and the metric used to measure it.
• Articulate when not to use an LLM — and what simpler approach they chose instead.
• Explain a security decision they made in an API or cloud deployment: the threat they were mitigating and why they chose that control.
  
  

What We Offer

• Direct involvement in enterprise AI implementations, from architecture to production.
• Exposure to a modern AI stack across multiple cloud platforms and LLM providers.
• Collaborative team environment with meaningful technical ownership.
• Hybrid working arrangements in Kuala Lumpur.
• Clear career growth path in a discipline with strong market demand.