Randstad Malaysia Hiring! Full Time Mid - Senior AI Engineer (Agentic AI - LLM Systems) in Federal Territory - Ricebowl

About The Role

We are looking to hire both a Mid-Level AI Engineer and a Senior AI Engineer to join our client's growing technical team.

In these roles, you will design, build, and deploy intelligent AI systems, with a core focus on Agentic AI, LLM-driven architectures, and conversational AI (including Claude AI). You will handle the full lifecycle of AI applications, from defining system architecture and complete process flows to Docker-based deployment and performance monitoring.

Note: Responsibilities and architectural ownership will scale based on the level (Mid vs. Senior) you are hired into.

Key Responsibilities

For Mid-Level AI Engineers

• Development: Write clean, efficient, and scalable Python code to build LLM applications, custom agents, and chatbot features.
• Process Flow Integration: Help implement end-to-end data pipelines, vector search, and agentic workflows under guided architecture.
• Deployment & Containerization: Use Docker to containerize applications and assist with production deployments.
  
  

For Senior AI Engineers

• System Architecture: Lead the design and implementation of end-to-end process flows, system architectures, and multi-agent AI ecosystems.
• Production Deployment: Own the deployment, hosting, and monitoring pipelines for large language models and complex agentic systems.
• Technical Leadership: Make core technical decisions, review code, and ensure high system reliability, low latency, and scalable performance.
  
  

Requirements

• Experience:
• Mid-Level Role: Minimum 2+ years of hands-on experience developing and deploying AI/LLM applications.
• Senior Role: 4+ years of professional experience in software engineering, with a strong track record of leading AI system design and production deployments.
• Core Tech Stack: Advanced proficiency in Python and solid hands-on experience with Docker.
• AI & LLM Domain: Proven experience working with LLMs (e.g., Claude AI), Agentic AI concepts, and chatbot architectures.
• End-to-End Delivery: Demonstrated experience taking AI projects across the entire process flow—from design to production deployment.
  

Nice to Have

• Experience with orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI).
• Hands-on experience with cloud infrastructure (AWS, GCP, or Azure).
• Experience with vector databases and model fine-tuning techniques.