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.

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.