AI Engineer

Starhub; Singapore; Presencial; SGD 120.000 - 180.000; Jornada completa

Descripción de la vacante

StarHub Ltd is seeking an on-site AI Engineer to merge generative AI with mobile network operations. You will drive zero-touch automation, predictive maintenance, and autonomous operations by building robust GenAI pipelines and validating outputs against telecom telemetry.

The role requires hands-on ML/NLP experience (1–3 years) and a strong foundation in Python, data science, and ML tooling. Expect collaboration across cross-functional teams and a security‑minded mindset.

Formación

• 2+ years hands-on experience with mobile networks (4G/LTE, 5G Core architectures, RAN, IMS, Transport, Network Management).
• AI/ML/NLP/Data Science engineering experience (1–3 years), including production-like work or internships.
• Bachelor’s degree in CS, Data Science, AI, or related quantitative field.

Responsabilidades

• Deploy and fine-tune GenAI & RAG pipelines using telecom telemetry and 3GPP docs.
• Engineer time-series models for traffic forecasting and anomaly detection.
• Package models as containerized microservices and manage high-throughput serving.
• Interface AI workflows with OSS/BSS platforms and implement guardrails.
• Collaborate with Data Engineers and RF specialists to validate model outputs.
• Apply basic cybersecurity practices, ensure data privacy and secure endpoints.

Conocimientos

Python

SQL

Pandas

NumPy

Scikit-learn

SciPy

Educación

Bachelor’s in Computer Science / Data Science / AI

Herramientas

Docker

Kubernetes

MLflow

Weights & Biases

vLLM

Triton

Descripción del empleo

Select how often (in days) to receive an alert:

Date: 20 Oct 2026

Location: SG

Job Description

This on-premises AI Engineer role merges generative AI and mobile network operations to drive zero-touch automation, predictive maintenance and Autonomous operations objectives.

Core Responsibilities

• GenAI & RAG: Deploy, fine-tune (LoRA/QLoRA), LangGraph, Agentic and optimize foundational models and Telecom Language Models (TLMs). Build robust RAG pipelines and autonomous workflows using telecom telemetry (PM, CM, FM, XDRs) and 3GPP documentation.
• Predictive Analytics: Engineer time-series and deep learning models for traffic forecasting, dynamic anomaly detection, and automated root cause analysis (RCA) for network degradations.
• MLOps & Deployment: Package models as containerized microservices (Docker/Kubernetes). Manage high-throughput serving runtimes (vLLM, Triton), GPU scheduling, and experiment tracking (MLflow, W&B).
• Platform Integration & Guardrails: Interface AI workflows with enterprise OSS/BSS platforms (IBM watsonX, ServiceNow). Implement strict hallucination mitigation, output guardrails, and model drift detection.
• Cross-Domain Collaboration: Partner with Data Engineers for telemetry ingestion and collaborate with RAN/Core/Transport specialists to validate model outputs against real-world RF behaviour.
• CyberSecurity: Applying basic cybersecurity principles, ensuring data privacy, secure API endpoints and prompt security.

Qualifications

• Preferably with Network Expertise: 2+ years of hands-on experience with mobile networks (4G/LTE, 5G Core architectures, RAN, IMS, Transport, Network Management etc) and Telecom Protocols (Diameter, GTP, SIP, HTTP2, SCTP, PFCP).
• AI/ML Experience: 1–3 years in applied ML/NLP/Data Science engineering (can include production-level internships or heavy applied research).
• Education: Bachelor’s in Computer Science, Data Science, AI, or a related quantitative field.

Detail

Information

Languages & Data

Python, SQL, pandas, numpy, scikit-learn, scipy

AI/ML & NLP

DevOps & Serving

Telecom Knowledge

RAN/EPC/5GC architecture, critical KPIs (RSRP, SINR, CQI, latency), Telecom protocols

Preferred Assets

• Experience with open-source/proprietary Telecom Language Models and datasets (3GPP, ITU).
• Familiarity with model quantization (GPTQ, AWQ, GGUF) for edge inference.
• Exposure to real-time streaming (Kafka, Flink) and data lakehouses (Iceberg).
• Understanding of telecom AI standardization (O-RAN, NWDAF).

The ideal candidate is a hybrid specialist who can comfortably debate 5G protocol stacks in one breath and optimize a vector database retrieval pipeline in the next.