Our client, a multinational telecom technology company, is looking for two Lead GenAI / AI-ML SME's to drive technical strategy on a high-visibility agentic AI program. This is a chance to architect solutions that sit at the intersection of large language models and real-world network operations, with direct influence on how the company's infrastructure is monitored, diagnosed, and improved.

Why This Role Matters:

You will not just implement AI, you will define how it works. As the go-to SME for a network anomaly detection initiative, you will guide technical direction, mentor engineering teams, and see your architecture decisions deployed at scale. This is a high-impact, high-visibility position for someone who wants ownership over cutting-edge GenAI systems rather than a narrow slice of one.

What You Will Do:

* Serve as the AI/ML SME for a network anomaly agentic AI program, shaping solution design and implementation strategy from the ground up
* Design and build LLM-powered agents and multi-agent workflows for anomaly detection, triage, root-cause investigation, and operational insight generation
* Develop RAG capabilities that connect network data, documentation, incident history, and knowledge bases into a unified intelligence layer
* Lead the technical playbook for prompt engineering, tool-calling, agent routing, structured outputs, and evaluation frameworks that keep AI-assisted operations reliable and trustworthy
* Partner with architects, engineers, data teams, product owners, and client stakeholders to translate business and network operations needs into working AI capabilities
* Drive code reviews, architecture discussions, and sprint delivery while sharing knowledge across the broader GenAI engineering team



If you would like to learn more, please reach out to Elite Technical.

Required Skills

Required:
- 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.
- Working knowledge of cloud AI services, APIs, databases, observability, testing, and production deployment practices.

Preferred:
- Experience with network operations, anomaly detection, incident analysis, telecommunications, or enterprise operations use cases preferred.
- 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.
- Network, telecom, or operations-focused certifications are a plus.