GenAI Engineer — Network Ops & RAG Pipelines

MACHINE LEARNING TECHNOLOGIES LLC; Palo Alto (CA); Presencial; USD 160.000 - 240.000; Jornada completa

Descripción de la vacante

MACHINE LEARNING TECHNOLOGIES LLC is seeking an AI Engineer to build GenAI-powered tools for network troubleshooting in Palo Alto. You will design RAG pipelines, embeddings, vector search, and grounding with citations, aiming for reliable, scalable services.

You will productionize models and prompts with CI/CD, automated tests, monitoring, and cost controls while collaborating with data engineering, security, and operations teams to deliver measurable impact.

Formación

• BS/MS in computer science, electrical engineering, data science, or related field.
• 3-5 years of AI/ML engineering, data engineering, or applied data science delivering production-grade solutions.
• Strong Python and SQL skills with large-scale telemetry/time-series datasets.
• Experience with Azure data/AI solutions (Azure ML, Azure AI Services/OpenAI, Databricks).
• Familiarity with MLOps, CI/CD, testing, and observability.

Responsabilidades

• Build, deploy, and operate GenAI-powered tools for network troubleshooting with citations.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, embeddings, and vector search.
• Productionize models/prompts with CI/CD, tests, canary releases, monitoring and SLOs.
• Implement evaluation, drift detection, and cost governance for GenAI systems.
• Integrate with internal systems, alarms, dashboards, and runbooks for remediation.

Conocimientos

Python

SQL

Azure ML

GenAI

MLOps

CI/CD

LLM

Educación

BS/MS in CS or related field

Herramientas

Azure Databricks

Azure OpenAI

Azure Data Lake

AKS

LangChain

Semantic Kernel

Descripción del empleo

MACHINE LEARNING TECHNOLOGIES LLC is seeking an AI Engineer to build GenAI-powered tools for network troubleshooting in Palo Alto. You will design RAG pipelines, embeddings, vector search, and grounding with citations, aiming for reliable, scalable services.

You will productionize models and prompts with CI/CD, automated tests, monitoring, and cost controls while collaborating with data engineering, security, and operations teams to deliver measurable impact.