AI Transformation Engineer

Starhub Ltd; Singapore; On-site; SGD 52,000 - 90,000; Full time

Job summary

StarHub Ltd. invites an AI Transformation Engineer to translate AI ideas into practical solutions for network and operations teams. You will work hands-on with senior engineers to build, test, and deploy use cases that automate workflows and surface insights from data.

This early-career role offers broad exposure to data pipelines, machine learning, LLM-based tooling, and process automation, while ensuring solutions are reliable, compliant, and auditable as per PDPA guidelines.

Qualifications

• Degree in CS/DS/Engineering or related field.
• 0–2 years; internships welcome in AI, data, or automation.
• Working knowledge of Python and ML; basic LLM concepts.
• Exposure to telecom or network data, cloud or LLM tooling.
• Foundational familiarity across Python, Git, APIs, Pandas/NumPy

Responsibilities

• Build and deliver AI solutions; support design, development and testing.
• Build and maintain data pipelines for AI models.
• Develop prompts and lightweight LLM integrations.
• Identify repetitive tasks and automate workflows.
• Implement production automations and monitor performance.
• Run experiments and measure model accuracy, latency, and reliability.
• Document data sources and design decisions; ensure reproducibility.
• Collaborate with data, network, and platform engineers; participate in reviews.
• Follow PDPA and internal data-governance practices.

Skills

Python

ML Fundamentals

LLMs/SLMs

RAG/Prompting

Data Pipelines

REST APIs

Cloud Basics

Git/Version Control

Pandas/NumPy

Education

Bachelor's degree in CS/DS/Engineering

Tools

LLM Tooling

Cloud Platforms

Data Visualization

Job description

AI Transformation Engineer

Date: 9 Jul 2026

Location: SG

Job Description

Role Overview

As a AI Transformation Engineer, you will help translate AI and automation ideas into working solutions that improve how StarHub's network and operations teams run day to day. You will work alongside senior engineers to build, test, and deploy practical AI use cases — from automating manual workflows to surfacing insights from network and operational data.

This is a hands‑on, learning‑focused role for an early‑career engineer. You will get broad exposure across the technology transformation — data pipelines, machine learning, LLM‑based tooling, and process automation — while building the judgement to deliver solutions that are reliable, compliant, and genuinely useful.

Key Responsibilities

1. Building & Delivering AI Solutions

• Support the design, development, and testing of AI and automation use cases under the guidance of senior engineers.
• Build and maintain data pipelines and scripts that prepare, clean, and transform network and operational data for AI models.
• Develop and refine prompts, retrieval (RAG) workflows, and lightweight integrations for LLM‑based internal tools.

2. Automation & Process Improvement

• Identify repetitive manual tasks across engineering and operations that are good candidates for automation.
• Implement workflow automations and monitor them in production, escalating issues and iterating on improvements.

3. Data, Testing & Documentation

• Run experiments, evaluate model outputs, and help measure accuracy, latency, and reliability against clear success criteria.
• Document solutions, data sources, and design decisions so work is reproducible and auditable.
• Work closely with data, network, and platform engineers, and take part in code reviews and knowledge‑sharing.
• Follow PDPA and internal data‑governance practices when handling customer and network data.

Qualifications

• Degree (or final‑year / recent graduate) in Computer Science, Data Science, Engineering, or a related field.
• 0–2 years; internships, academic projects, or personal projects in AI, data, or automation are welcomed.
• Working knowledge of Python, ML and AI; basic understanding of how LLMs and SLMs are used in applications.
• Exposure to telecom or network data, cloud platforms, or LLM tooling (e.g. RAG, prompting) through study or projects.
• Foundational familiarity in: Python/AI Agents/Git/Version Control/Rest APIs/ML Fundamentals/LLMs/SLMs/Prompting/RAG Basics/Pandas/NumPy/Data pipelines/Cloud Basics/Automation Scripting/Data Visualization