AI Engineer
At a glance
Mid-level AI Engineering role at StarHub. Singapore.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build RAG systems and agent workflows using telecom network data and 3GPP documentation
- Train and tune language models, then develop forecasting and anomaly detection for network operations
- Package models for deployment and manage serving, GPU resources, and experiment tracking
- Connect AI workflows to OSS/BSS platforms and add safeguards against hallucinations and model drift
- Work with data engineers and network specialists to check outputs against observed radio behavior
What you bring
- You have one to three years in applied machine learning, NLP, or data science engineering
- You bring at least two years working directly with mobile networks and telecom protocols
- A bachelor's degree in computer science, data science, AI, or a related quantitative discipline
- You can work with the listed AI, data, and deployment tools, including Python, PyTorch or TensorFlow, and Docker
Who this fits
This mid-level role suits someone who can combine applied AI engineering with practical mobile-network knowledge. The work is on-premises and includes collaboration across data engineering and RAN, core, and transport teams.
From the employer
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
PyTorch/TensorFlow, Hugging Face, PEFT/LoRA, RAG evaluation
Vector & Big Data
Chroma, Weaviate, pgvector, PySpark, Snowflake
DevOps & Serving
Git, Docker, Kubernetes, FastAPI, vLLM, MLflow, W&B
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).
- Active public portfolio (GitHub, Hugging Face, technical blogs).
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.
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AI Engineering
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