Description

You build, deploy, and operate GenAI tools to accelerate network troubleshooting, including triage assistants, KPI summaries, and anomaly explanations with source citations.

Responsibilities

• Design and implement Retrieval-Augmented Generation (RAG) pipelines covering document preparation, embeddings, vector search, grounding, and safety guardrails.
• Productionize models and prompts using CI/CD, automated testing, and canary/A–B releases while managing SLOs for accuracy, latency, and cost.
• Implement continuous evaluation harnesses, including offline/online testing, golden sets, and prompt/knowledge drift detection.
• Integrate AI services with internal systems like ticketing platforms, KPI dashboards, and topology APIs to close the detection-to-remediation loop.
• Collaborate across data engineering and platform teams to move use cases from discovery through measurable production impact.

Required Skills

• 3–5 years of experience delivering production-grade AI/ML solutions.
• Strong proficiency in Python and SQL, specifically handling large-scale telemetry/time-series data transformations.
• Hands-on experience with Azure AI Services and Azure OpenAI for LLM/GenAI applications.
• Working knowledge of RAG patterns: chunking, embeddings, vector stores, and grounding strategies.
• Experience with frameworks such as Semantic Kernel and Lang Chain.
• Proficiency in building reliable, testable data pipelines using tools like Azure Data Factory and Delta Lake.
• Familiarity with operational practices, including logging, monitoring, and security basics (RBAC, PII handling).
• Ability to translate complex network issues (4G/5G) into measurable KPIs and actionable engineering workflows.
• Experience with version control using Git.

Key Skills

Education

Any Graduate