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Open nowPosted yesterday

AI Engineer

RED DOT AI PTE. LTD.

Pay
SGD 5,000 – SGD 7,500 a month
Where
Central, Singapore
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Your applicationOpen nowAI EngineerRED DOT AI PTE. LTD. · Central, Singapore

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  2. 4.5%3 days
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  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: posted yesterday

The posting

AI Engineer (LLM & Agentic AI)

CompanyOverview

Red Dot AI is a Singapore-based deep-tech company incubated by Nanyang Technological University and certified by IMDA.With physics-driven digital twins, AI agents, and real-time infrastructure intelligence at its core, Red Dot AI helps data center and critical infrastructure customers turn complex operational data into actionable decisions, optimizing compute, energy use, cooling, and operations.

Role Overview

We are hiring an AI Engineer for a full-time role in Singapore to build LLM-powered agents and co-pilots for datacenter operations. You will connect language models, digital twins, and real-time data to support operational decisions, energy optimization, and infrastructure automation.

You will work with data scientists, systems engineers, and data center specialists to turn AI research into working applications. The role combines hands-on backend development with agent design, integration, and evaluation in simulation and real operating environments.

KeyResponsibilities

1. LLM Applications and AgentDevelopment

· Design, develop, and deploy agentic systems and LLM-powered co-pilots for data center use cases.

· Build the backend services and application logic needed to run these systems reliably at scale.

2. Agent Architectures and Tool Use

· Develop agent architectures that combine external tools, planning, and multi-agent collaboration.

· Apply retrieval-augmented generation (RAG) and prompt engineering to connect agents with relevant knowledge and support simulation-based optimization.

3. Digital Twin and Real-Time DataIntegration

· Integrate AI agents with digital twin platforms, telemetry pipelines, and domain-specific simulation models.

· Connect model outputs and operational data with agent workflows for energy optimization and operational decision support.

4. Evaluation and Iterative Improvement

· Evaluate and benchmark agents in simulated and real-world environments for reliability and efficiency.

· Use evaluation findings to refine agent behavior, tool use, and application performance.

5. Research and Cross-FunctionalCollaboration

· Work with data scientists, systems engineers, and domain experts to translate operational needs into AI applications.

· Follow advances in LLMs, reinforcement learning, and agentic systems; share findings through publications and internal knowledge-sharing.

Requirements

· A degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.

· Proven backend development experience and the ability to build reliable, scalable systems.

· Hands-on experience developing applications with LLMs, agentic frameworks, and/or generative AI.

· Expert-level Python programming skills.

· Deep familiarity with core MLand LLM libraries or APIs, such as PyTorch, LangChain, and OpenAI APIs.

· Practical knowledge of RAG, prompt engineering, and agents that use external tools.

PreferredQualifications

· Familiarity with distributed systems, data engineering, or digital twin platforms.

· Experience with telemetry data, time-series analysis, or IoT device integration.

· A background in industrial automation, robotics, or control systems.

· Contributions to open-source AI projects or a portfolio of relevant personal projects.

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