Description

Build, deploy, and operate GenAI-powered tools that accelerate network troubleshooting and remediation.

This role is on-site.

Responsibilities

• Design and implement RAG pipelines, including document chunking, embeddings, vector search with re-ranking, and citation strategies.
• Ship reliable services by productionizing models and prompts using CI/CD, automated tests, canary releases, and SLOs for accuracy and latency.
• Implement evaluation harnesses and continuous monitoring for offline/online testing, drift detection, and cost/token budgeting.
• Integrate with internal systems such as alarm platforms, ticketing, and topology APIs to close the loop from detection to remediation.
• Collaborate with data engineering and platform teams to translate network issues into measurable KPIs and actionable outputs.

Required Skills

• 3–5 years of experience in AI/ML engineering, data engineering, or applied data science delivering production-grade solutions.
• Strong Python and SQL skills; mastery working with large-scale telemetry and time-series datasets.
• Hands-on experience with Azure AI Services, Azure OpenAI, and Azure Machine Learning.
• Deep understanding of Retrieval-Augmented Generation (RAG), including hybrid search, grounding, and vector stores like Azure AI Search.
• Experience with prompt design, few-shot patterns, structured outputs, and function/tool calling.
• Production mindset with robust logging, tracing, observability, and security basics (RBAC, Key Vault, PII handling).
• BS/MS in Computer Science, Electrical Engineering, Data Science, or equivalent practical experience.

Preferred Skills

• Experience with agentic workflows, orchestration, and LLMops practices like MLOps/LLMOps for versioning and automated evaluations.
• Solid understanding of 4G/5G RAN and mobility concepts (handovers, drops, throughput, KPIs) and telecom standards.
• Familiarity with GenAI frameworks (Semantic Kernel, LangChain/LlamaIndex) and containerized deployments on AKS.

Key Skills

Education

Any Graduate