Description

You will build, deploy, and operate GenAI tools to accelerate network troubleshooting, including triage assistants, KPI summaries, and anomaly detection.

Responsibilities

• Design and implement Retrieval-Augmented Generation (RAG) pipelines, handling document chunking, embeddings, vector search, and citation strategies.
• Productionize models and prompts using CI/CD, automated testing, and implementing SLOs for accuracy, grounding, and latency.
• Implement offline and online evaluation harnesses to monitor prompt/knowledge drift and maintain token/cost budgets.
• Integrate GenAI services with internal systems like alarm platforms, ticketing, and inventory APIs to close the remediation loop.
• Collaborate with data engineering and RAN SMEs to move use cases from discovery through measurable production impact.

Required Skills

• 3–5 years of experience in AI/ML engineering or applied data science delivering production-grade solutions.
• Strong proficiency in Python and SQL, with experience handling large-scale telemetry/time-series datasets.
• Hands-on experience with Azure services for data/AI, including Azure Machine Learning and Azure AI Services/Azure OpenAI.
• Experience implementing RAG patterns: chunking, embeddings, vector stores (e.g., Azure AI Search), and grounding.
• Proficiency in CI/CD pipelines for ML/LLM services and operational monitoring practices.
• Familiarity with GenAI development patterns like prompt design (few-shot, JSON Schema) and function/tool calling.
• Knowledge of core network concepts (4G/5G RAN, mobility, KPIs) and translating them into actionable outputs.
• Experience with Git, Azure Data Factory, and Azure Data Lake.
• Understanding of MLOps/LLMOps practices, including versioning and automated evaluations.

Key Skills

Education

Any Graduate