AI Engineer

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.