Description

Build, deploy, and operate GenAI tools that accelerate network troubleshooting, including triage assistants, KPI summaries, anomaly explanations, and recommended next actions with source citations.

Responsibilities

• Design and implement Retrieval-Augmented Generation (RAG) pipelines, managing document preparation, embeddings, vector search, and grounding strategies.
• Productionize models and prompts using CI/CD, implementing automated tests, canary releases, and setting SLOs for accuracy, latency, and cost.
• Implement evaluation frameworks, including offline/online testing, golden sets, and drift detection for prompt and knowledge.
• Integrate AI services with internal platforms like ticketing, alarm systems, and topology APIs to automate detection-to-remediation loops.
• Collaborate cross-functionally to move use cases from discovery through measurable production impact.

Required Skills

• 3–5 years of experience in AI/ML engineering, data 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.
• Proven experience implementing RAG patterns, including chunking, embeddings, and vector search.
• Familiarity with CI/CD Tooling for deploying machine learning pipelines and services.
• Understanding of GenAI development patterns, including prompt design (few-shot, structured outputs) and function/tool calling.
• Experience operationalizing AI, including robust logging, monitoring, and tracing.
• Solid understanding of 4G/5G RAN and mobility concepts.
• Ability to translate network issues into measurable KPIs and actionable workflows.

Key Skills

Education

Any Graduate