AI Transformation Engineer

StarHub Ltd; Singapore; Vor Ort; SGD 65.000 - 95.000; Vollzeit

Zusammenfassung

StarHub Ltd is seeking an AI Transformation Engineer to translate AI and automation ideas into practical solutions that improve how network and operations teams run day to day. This hands-on role is ideal for an early-career engineer eager to learn across data pipelines, ML, LLM tooling, and process automation.

You will build, test, and deploy AI solutions in collaboration with senior engineers, while ensuring reliability, compliance, and usefulness of the delivered tooling.

Qualifikationen

• 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 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 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

Aufgaben

• Build, test, and deploy AI/automation use cases under guidance of senior engineers.
• Develop data pipelines and scripts to prepare network and operational data for AI models.
• Refine prompts, retrieval workflows, and lightweight integrations for internal tools.
• Identify repetitive manual tasks and implement workflow automations.
• Document data sources, design decisions, and solutions for reproducibility.
• Collaborate with data, network, and platform engineers; participate in code reviews and governance.

Kenntnisse

Python

ML/AI basics

LLMs/SLMs basics

RAG tooling

Data pipelines

Git/Version Control

Rest APIs

Pandas/NumPy

Cloud basics

Automation scripting

Ausbildung

Degree in Computer Science/Data Science/Engineering or related field

Tools

RAG tooling

LLM tooling

Jobbeschreibung

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.

4. Collaboration & Compliance

• 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