Lead GenAI / AI-ML SME

Role Summary

hands-on AI/ML SME with strong software engineering discipline, deep GenAI delivery experience, and the consulting skills needed to translate network operations requirements into scalable agentic AI capabilities.

Production monitoring experience is a valuable secondary skill, but the primary focus is AI solution architecture, LLM agent development, RAG design, and network anomaly use-case delivery.

Responsibilities

• Serve as the AI/ML SME for a network anomaly agentic AI program, advising on solution design, technical direction, and implementation approach.
• Design and develop LLM-powered agents and multi-agent workflows for network anomaly detection, triage, root-cause investigation, and operational insight generation.
• Build RAG capabilities using network data, documentation, incident history, knowledge bases, and operational procedures.
• Lead prompt engineering, tool-calling, agent routing, structured outputs, and evaluation patterns for reliable AI-assisted network operations.
• Partner with architects, engineers, data teams, product owners, and client stakeholders to convert business and network operations needs into actionable AI capabilities.
• Support code reviews, architecture discussions, sprint delivery, documentation, and knowledge sharing across the GenAI engineering team.

Requirements and skills

• Strong Python software engineering experience with hands-on GenAI, AI/ML, or LLM-based application delivery.
• Experience with LangChain, LangGraph, or equivalent agentic AI frameworks.
• Strong understanding of LLM APIs, tool calling, prompt engineering, structured outputs, streaming, and agent orchestration patterns.
• Experience designing RAG solutions using embeddings, vector search, hybrid retrieval, re-ranking, and answer generation.
• Ability to operate as a technical SME with strong communication, consulting, solution design, and stakeholder-facing skills.
• Experience with network operations, anomaly detection, incident analysis, telecommunications, or enterprise operations use cases preferred.
• Working knowledge of cloud AI services, APIs, databases, observability, testing, and production deployment practices.

Preferred Certifications

• AWS, Azure, or Google Cloud AI/ML certification preferred.
• Relevant certification or training in artificial intelligence, machine learning, data engineering, cloud architecture, or software engineering preferred.