GenAI Engineer - Network Ops & RAG Pipelines

Machine Learning Technologies, LLC; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Machine Learning Technologies, LLC in Palo Alto, CA is seeking an AI/ML engineer to build GenAI-powered network troubleshooting tools. You will triage issues, generate KPI summaries with citated data, and design RAG pipelines with embedding and vector search.

Responsibilities include productionizing models with CI/CD, automated tests, canaries, and monitoring; implementing evaluation harnesses; and collaborating with data engineering, security, and RAN SMEs to deliver measurable improvements in

Qualifications

• 3–5 years of AI/ML engineering or data science delivering production-grade solutions.
• Strong Python and SQL skills.
• Experience with GenAI tooling and MLOps practices.
• Ability to translate network issues into KPI-driven workflows.
• Familiarity with Azure AI services and CI/CD for ML pipelines.

Responsibilities

• Build GenAI-powered troubleshooting tools and KPI summaries with citations.
• Design and implement RAG pipelines and grounding strategies.
• Productionize models with CI/CD, tests, canaries, and monitoring.
• Develop evaluation harnesses and drift detection mechanisms.
• Collaborate with data engineering and security teams to deliver end-to-end solutions.
• Translate network issues into actionable RCA steps and remediation plans.

Skills

Python

SQL

GenAI development

RAG pipelines

Prompt design

Observability

Security basics

Education

BS in CS, EE, DS, or related field

Tools

Azure Functions

Azure DevOps

LangChain

LlamaIndex

Vector databases

Job description

Machine Learning Technologies, LLC in Palo Alto, CA is seeking an AI/ML engineer to build GenAI-powered network troubleshooting tools. You will triage issues, generate KPI summaries with citated data, and design RAG pipelines with embedding and vector search.

Responsibilities include productionizing models with CI/CD, automated tests, canaries, and monitoring; implementing evaluation harnesses; and collaborating with data engineering, security, and RAN SMEs to deliver measurable improvements in