Description

You build, deploy, and operate GenAI-powered tools that accelerate network troubleshooting, including triage assistants, KPI summaries, anomaly detection, and recommended next actions with source data citations.

Responsibilities

• Design and implement RAG pipelines, handling document preparation, embeddings, vector search, grounding, and citation strategies.
• Productionize models and prompts using CI/CD, automated testing, and canary/A–B releases while adhering to SLOs for accuracy and latency.
• Implement evaluation harnesses for offline and online testing, managing golden sets and drift detection.
• Integrate AI services with internal systems like alarms, ticketing platforms, and topology APIs to close the detection-to-remediation loop.
• Collaborate across data engineering, platform, and RAN SMEs to advance 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.
• Working knowledge of GenAI patterns: RAG (chunking, embeddings, hybrid search), prompt design (function calling, JSON Schema), and evaluation fundamentals.
• Experience with LLM frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
• Solid understanding of 4G/5G RAN and mobility concepts (e.g., handover, SINR).
• Experience building reliable, testable data transformations and implementing operational readiness patterns (logging, tracing, caching).
• Familiarity with vector stores (e.g., Azure AI Search) and grounding strategies.

Key Skills

Education

Any Graduate