About this role
Summary: Builds generative AI and predictive analytics systems that automate telecom network operations using telemetry and 3GPP documentation. Deploys models into production guardrails while collaborating with data and RAN teams.
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.